Project Overview/Objectives:

Context:

There is a huge demand for used cars in the Indian Market today. As sales of new cars have slowed down in the recent past, the pre-owned car market has continued to grow over the past years and is larger than the new car market now. Cars4U is a budding tech start-up that aims to find footholes in this market.

In 2018-19, while new car sales were recorded at 3.6 million units, around 4 million second-hand cars were bought and sold. There is a slowdown in new car sales and that could mean that the demand is shifting towards the pre-owned market. In fact, some car sellers replace their old cars with pre-owned cars instead of buying new ones. Unlike new cars, where price and supply are fairly deterministic and managed by OEMs (Original Equipment Manufacturer / except for dealership level discounts which come into play only in the last stage of the customer journey), used cars are very different beasts with huge uncertainty in both pricing and supply. Keeping this in mind, the pricing scheme of these used cars becomes important in order to grow in the market.

As a senior data scientist at Cars4U, you have to come up with a pricing model that can effectively predict the price of used cars and can help the business in devising profitable strategies using differential pricing. For example, if the business knows the market price, it will never sell anything below it.

Objective:

To explore and visualize the dataset, build a linear regression model to predict the prices of used cars, and generate a set of insights and recommendations that will help the business.

Data Dictionary (Feature : Explaination )

The data contains the different attributes of used cars sold in different locations. The detailed data dictionary is given below.

  • S.No. : Serial number

  • Name : Name of the car which includes brand name and model name

  • Location : Location in which the car is being sold or is available for purchase (cities)

  • Year : Manufacturing year of the car

  • Kilometers_driven : The total kilometers (a unit used to measure length or distance) driven in the car by the previous owner(s)

  • Fuel_Type : The type of fuel used by the car (Petrol, Diesel, Electric, CNG, LPG)

  • Transmission : The type of transmission used by the car (Automatic/Manual)

  • Owner : Type of ownership

  • Mileage : The standard mileage offered by the car company in kmpl or km/kg

  • Engine : The displacement volume of the engine in CC

  • Power : The maximum power of the engine in bhp

  • Seats : The number of seats in the car

  • New_Price : The price of a new car of the same model in INR Lakhs (1 Lakh INR = 100,000 INR)

  • Price : The price of the used car in INR Lakhs

Scoring Rubric & Best Practices for Notebook

  • The notebook should be well-documented, with inline comments explaining the functionality of code and markdown cells containing comments on the observations and insights.

  • The notebook should be run from start to finish in a sequential manner before submission.

  • It is preferable to remove all warnings and errors before submission. The notebook should be submitted as an HTML file (.html) and NOT as a notebook file (.ipynb).

  1. Define the problem and perform an Exploratory Data Analysis: Problem definition, questions to be answered - Data background and contents - Univariate analysis - Bivariate analysis (6 points)
  2. Illustrate the insights based on EDA: Key meaningful observations on the relationship between variables (3 points)
  3. Data pre-processing: Prepare the data for analysis and modeling - Missing value Treatment - Outlier Treatment - Feature Engineering (10 points)
  4. Model building - Linear Regression: Build the model and comment on the model performance (8 points)
  5. Model performance evaluation: Evaluate the model on different performance metrics (4 points)
  6. Actionable Insights & Recommendations: Conclude with the key takeaways for the business (5 points)
  7. Notebook - Overall Quality: Structure and flow - Well-commented code (4 points)
In [1]:
# importing all of the required libraries

import numpy as np   
import pandas as pd   

import matplotlib.pyplot as plt 
%matplotlib inline 
import seaborn as sns

from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squared_error
In [2]:
# loading the data set
pd.set_option('display.max_columns', None)

from google.colab import files
data_to_load = files.upload()
Upload widget is only available when the cell has been executed in the current browser session. Please rerun this cell to enable.
Saving used_cars_data.csv to used_cars_data (1).csv
In [3]:
import io
df = pd.read_csv(io.BytesIO(data_to_load['used_cars_data.csv']))

Objective:

  • Analyze and get an understanding of the dataset
  • Prepare the dataset for modeling
  • Create a model to predict the price of a used car, evaluate the model based on different metrics, and conclude with key business insights

We will start with EDA and Data Preprocessing to understand the data and prepare it for modelling.

Taking an initial look at the dataset

In [4]:
# checking the shape of the data set
print(f'There are {df.shape[0]} rows and {df.shape[1]} columns.')
There are 7253 rows and 14 columns.
In [5]:
df.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 7253 entries, 0 to 7252
Data columns (total 14 columns):
 #   Column             Non-Null Count  Dtype  
---  ------             --------------  -----  
 0   S.No.              7253 non-null   int64  
 1   Name               7253 non-null   object 
 2   Location           7253 non-null   object 
 3   Year               7253 non-null   int64  
 4   Kilometers_Driven  7253 non-null   int64  
 5   Fuel_Type          7253 non-null   object 
 6   Transmission       7253 non-null   object 
 7   Owner_Type         7253 non-null   object 
 8   Mileage            7251 non-null   object 
 9   Engine             7207 non-null   object 
 10  Power              7078 non-null   object 
 11  Seats              7200 non-null   float64
 12  New_Price          7253 non-null   float64
 13  Price              6019 non-null   float64
dtypes: float64(3), int64(3), object(8)
memory usage: 793.4+ KB
In [6]:
df.dtypes
Out[6]:
S.No.                  int64
Name                  object
Location              object
Year                   int64
Kilometers_Driven      int64
Fuel_Type             object
Transmission          object
Owner_Type            object
Mileage               object
Engine                object
Power                 object
Seats                float64
New_Price            float64
Price                float64
dtype: object
In [7]:
df.isnull().sum().sort_values(ascending=False)
Out[7]:
Price                1234
Power                 175
Seats                  53
Engine                 46
Mileage                 2
S.No.                   0
Name                    0
Location                0
Year                    0
Kilometers_Driven       0
Fuel_Type               0
Transmission            0
Owner_Type              0
New_Price               0
dtype: int64
In [8]:
# checking for duplicate entries in the data set
df.duplicated().sum()
Out[8]:
0
In [9]:
# dropping duplicates

df.drop_duplicates(inplace=True)

Observations

  • There are 7253 rows and 14 columns in the data set.
  • There are 3 columns of int datatype (S.No., Year, Kilometers_Driven).
  • There are 8 columns of object datatype (Name, Location, Fuel_Type, Transmission, Owner_Type, Mileage, Engine, Power).
  • There are 3 columns of float datatype (Seats, New_Price, Price).
  • There are 5 columns in the dataset with missing values (Price; 1234, Power: 175, Seats: 53, Engine: 46, Mileage: 2).
In [10]:
np.random.seed(1)
df.sample(n=10)
Out[10]:
S.No. Name Location Year Kilometers_Driven Fuel_Type Transmission Owner_Type Mileage Engine Power Seats New_Price Price
2397 2397 Ford EcoSport 1.5 Petrol Trend Kolkata 2016 21460 Petrol Manual First 17.0 kmpl 1497 CC 121.36 bhp 5.0 9.47 6.00
3777 3777 Maruti Wagon R VXI 1.2 Kochi 2015 49818 Petrol Manual First 21.5 kmpl 1197 CC 81.80 bhp 5.0 5.44 4.11
4425 4425 Ford Endeavour 4x2 XLT Hyderabad 2007 130000 Diesel Manual First 13.1 kmpl 2499 CC 141 bhp 7.0 35.29 6.00
3661 3661 Mercedes-Benz E-Class E250 CDI Avantgrade Coimbatore 2016 39753 Diesel Automatic First 13.0 kmpl 2143 CC 201.1 bhp 5.0 86.97 35.28
4514 4514 Hyundai Xcent 1.2 Kappa AT SX Option Kochi 2016 45560 Petrol Automatic First 16.9 kmpl 1197 CC 82 bhp 5.0 8.23 6.34
599 599 Toyota Innova Crysta 2.8 ZX AT Coimbatore 2019 40674 Diesel Automatic First 11.36 kmpl 2755 CC 171.5 bhp 7.0 28.05 24.82
186 186 Mercedes-Benz E-Class E250 CDI Avantgrade Bangalore 2014 37382 Diesel Automatic First 13.0 kmpl 2143 CC 201.1 bhp 5.0 86.97 32.00
305 305 Audi A6 2011-2015 2.0 TDI Premium Plus Kochi 2014 61726 Diesel Automatic First 17.68 kmpl 1968 CC 174.33 bhp 5.0 67.10 20.77
4582 4582 Hyundai i20 1.2 Magna Kolkata 2011 36000 Petrol Manual First 18.5 kmpl 1197 CC 80 bhp 5.0 10.25 2.50
5434 5434 Honda WR-V Edge Edition i-VTEC S Kochi 2019 13913 Petrol Manual First 17.5 kmpl 1199 CC 88.7 bhp 5.0 9.36 8.20
In [11]:
df.describe(include='all').T
Out[11]:
count unique top freq mean std min 25% 50% 75% max
S.No. 7253.0 NaN NaN NaN 3626.0 2093.905084 0.0 1813.0 3626.0 5439.0 7252.0
Name 7253 2041 Mahindra XUV500 W8 2WD 55 NaN NaN NaN NaN NaN NaN NaN
Location 7253 11 Mumbai 949 NaN NaN NaN NaN NaN NaN NaN
Year 7253.0 NaN NaN NaN 2013.365366 3.254421 1996.0 2011.0 2014.0 2016.0 2019.0
Kilometers_Driven 7253.0 NaN NaN NaN 58699.063146 84427.720583 171.0 34000.0 53416.0 73000.0 6500000.0
Fuel_Type 7253 5 Diesel 3852 NaN NaN NaN NaN NaN NaN NaN
Transmission 7253 2 Manual 5204 NaN NaN NaN NaN NaN NaN NaN
Owner_Type 7253 4 First 5952 NaN NaN NaN NaN NaN NaN NaN
Mileage 7251 450 17.0 kmpl 207 NaN NaN NaN NaN NaN NaN NaN
Engine 7207 150 1197 CC 732 NaN NaN NaN NaN NaN NaN NaN
Power 7078 385 74 bhp 280 NaN NaN NaN NaN NaN NaN NaN
Seats 7200.0 NaN NaN NaN 5.279722 0.81166 0.0 5.0 5.0 5.0 10.0
New_Price 7253.0 NaN NaN NaN 21.307322 24.256314 3.91 7.88 11.3 21.69 375.0
Price 6019.0 NaN NaN NaN 9.479468 11.187917 0.44 3.5 5.64 9.95 160.0

Observations

  • 2041 unque values for Name, most frequent is Mahindra XUV500 W8 2WD.
  • 11 unique values for Location, most frequent is Mumbai.
  • Values for Year range from 1996-2019.
  • Values for Kilometers_Driven range from 171-6,500,000.
  • 5 unique values for Fuel-Type, most frequent is Diesel.
  • 2 unique values for Transmission, most frequent is Manual.
  • 4 unique values for Owner_Type, most frequent is First.
  • 450 unique values for Mileage, most frequent is 17.0 kmpl.
  • 150 unique values for Engine, most frequent is 1197 CC.
  • 385 unique values for Power, most frequent is 74 bhp.
  • Values for Seats range from 0-10.
  • Values for New_Price range from 3.91-375.0 INR Lakhs.
  • Values for Price range from 0.44-160.0 INR Lakhs.

Now we will modify the individual columns as neccessary to prepare it for visualization and modeling.

In [12]:
# Dropping serial number column because it will not be neccessary for the model 

df.drop(['S.No.'], axis=1, inplace=True)
In [13]:
# Creating a new column for car age

current_year = 2022
df['Car_Age'] = current_year - df['Year']

df.head()
Out[13]:
Name Location Year Kilometers_Driven Fuel_Type Transmission Owner_Type Mileage Engine Power Seats New_Price Price Car_Age
0 Maruti Wagon R LXI CNG Mumbai 2010 72000 CNG Manual First 26.6 km/kg 998 CC 58.16 bhp 5.0 5.51 1.75 12
1 Hyundai Creta 1.6 CRDi SX Option Pune 2015 41000 Diesel Manual First 19.67 kmpl 1582 CC 126.2 bhp 5.0 16.06 12.50 7
2 Honda Jazz V Chennai 2011 46000 Petrol Manual First 18.2 kmpl 1199 CC 88.7 bhp 5.0 8.61 4.50 11
3 Maruti Ertiga VDI Chennai 2012 87000 Diesel Manual First 20.77 kmpl 1248 CC 88.76 bhp 7.0 11.27 6.00 10
4 Audi A4 New 2.0 TDI Multitronic Coimbatore 2013 40670 Diesel Automatic Second 15.2 kmpl 1968 CC 140.8 bhp 5.0 53.14 17.74 9
In [14]:
# Converting year column to datetime datatype

df['Year'] = pd.to_datetime(df['Year'], format='%Y')
In [15]:
#checking our changes

df.head()
Out[15]:
Name Location Year Kilometers_Driven Fuel_Type Transmission Owner_Type Mileage Engine Power Seats New_Price Price Car_Age
0 Maruti Wagon R LXI CNG Mumbai 2010-01-01 72000 CNG Manual First 26.6 km/kg 998 CC 58.16 bhp 5.0 5.51 1.75 12
1 Hyundai Creta 1.6 CRDi SX Option Pune 2015-01-01 41000 Diesel Manual First 19.67 kmpl 1582 CC 126.2 bhp 5.0 16.06 12.50 7
2 Honda Jazz V Chennai 2011-01-01 46000 Petrol Manual First 18.2 kmpl 1199 CC 88.7 bhp 5.0 8.61 4.50 11
3 Maruti Ertiga VDI Chennai 2012-01-01 87000 Diesel Manual First 20.77 kmpl 1248 CC 88.76 bhp 7.0 11.27 6.00 10
4 Audi A4 New 2.0 TDI Multitronic Coimbatore 2013-01-01 40670 Diesel Automatic Second 15.2 kmpl 1968 CC 140.8 bhp 5.0 53.14 17.74 9
In [16]:
# Splitting the name coluumn into Make and Model columns

# creating a column for car model
df['Car_Model'] = df.Name.str.split().str[0:3]

# joining the split list values in the car model column
df['Car_Model'] = df.Car_Model.apply(lambda x: ' '.join(x))

# creating a column for car brand
df['Car_Brand'] = df.Name.str.split().str[0]

# dropping the initial Name column
df.drop(['Name'], axis=1, inplace=True)

# taking a look at the changes made
df.head()
Out[16]:
Location Year Kilometers_Driven Fuel_Type Transmission Owner_Type Mileage Engine Power Seats New_Price Price Car_Age Car_Model Car_Brand
0 Mumbai 2010-01-01 72000 CNG Manual First 26.6 km/kg 998 CC 58.16 bhp 5.0 5.51 1.75 12 Maruti Wagon R Maruti
1 Pune 2015-01-01 41000 Diesel Manual First 19.67 kmpl 1582 CC 126.2 bhp 5.0 16.06 12.50 7 Hyundai Creta 1.6 Hyundai
2 Chennai 2011-01-01 46000 Petrol Manual First 18.2 kmpl 1199 CC 88.7 bhp 5.0 8.61 4.50 11 Honda Jazz V Honda
3 Chennai 2012-01-01 87000 Diesel Manual First 20.77 kmpl 1248 CC 88.76 bhp 7.0 11.27 6.00 10 Maruti Ertiga VDI Maruti
4 Coimbatore 2013-01-01 40670 Diesel Automatic Second 15.2 kmpl 1968 CC 140.8 bhp 5.0 53.14 17.74 9 Audi A4 New Audi

Next:

  • We need to turn Location, Fuel_Type, Transmission, Owner_Type into categorical columns.
  • We need to remove units from Mileage, Engine, and Power columns and convert to them float column.
In [17]:
# removing the units from Mileage, Engine, and Power and changing the coluumn datatype to float

df['Power'] = df.Power.str.split().str.get(0).astype('float64')
df['Engine'] = df.Engine.str.split().str.get(0).astype('float64')
df['Mileage'] = df.Mileage.str.split().str.get(0).astype('float64')

# taking a look at the changes we've made to confirm it worked correctly
df.head()
Out[17]:
Location Year Kilometers_Driven Fuel_Type Transmission Owner_Type Mileage Engine Power Seats New_Price Price Car_Age Car_Model Car_Brand
0 Mumbai 2010-01-01 72000 CNG Manual First 26.60 998.0 58.16 5.0 5.51 1.75 12 Maruti Wagon R Maruti
1 Pune 2015-01-01 41000 Diesel Manual First 19.67 1582.0 126.20 5.0 16.06 12.50 7 Hyundai Creta 1.6 Hyundai
2 Chennai 2011-01-01 46000 Petrol Manual First 18.20 1199.0 88.70 5.0 8.61 4.50 11 Honda Jazz V Honda
3 Chennai 2012-01-01 87000 Diesel Manual First 20.77 1248.0 88.76 7.0 11.27 6.00 10 Maruti Ertiga VDI Maruti
4 Coimbatore 2013-01-01 40670 Diesel Automatic Second 15.20 1968.0 140.80 5.0 53.14 17.74 9 Audi A4 New Audi
In [18]:
# converting Location, Fuel_Type, Transmission, and Owner_Type to categorical variables with one-hot encoding
df2 = pd.get_dummies(df, columns=['Location'])
df3 = pd.get_dummies(df2, columns=['Fuel_Type'])
df4 = pd.get_dummies(df3, columns=['Transmission'])
df5 = pd.get_dummies(df4, columns=['Owner_Type'])

#checking the changes made
df5.head()
Out[18]:
Year Kilometers_Driven Mileage Engine Power Seats New_Price Price Car_Age Car_Model Car_Brand Location_Ahmedabad Location_Bangalore Location_Chennai Location_Coimbatore Location_Delhi Location_Hyderabad Location_Jaipur Location_Kochi Location_Kolkata Location_Mumbai Location_Pune Fuel_Type_CNG Fuel_Type_Diesel Fuel_Type_Electric Fuel_Type_LPG Fuel_Type_Petrol Transmission_Automatic Transmission_Manual Owner_Type_First Owner_Type_Fourth & Above Owner_Type_Second Owner_Type_Third
0 2010-01-01 72000 26.60 998.0 58.16 5.0 5.51 1.75 12 Maruti Wagon R Maruti 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 1 1 0 0 0
1 2015-01-01 41000 19.67 1582.0 126.20 5.0 16.06 12.50 7 Hyundai Creta 1.6 Hyundai 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 1 1 0 0 0
2 2011-01-01 46000 18.20 1199.0 88.70 5.0 8.61 4.50 11 Honda Jazz V Honda 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 1 0 0 0
3 2012-01-01 87000 20.77 1248.0 88.76 7.0 11.27 6.00 10 Maruti Ertiga VDI Maruti 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 1 0 0 0
4 2013-01-01 40670 15.20 1968.0 140.80 5.0 53.14 17.74 9 Audi A4 New Audi 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 1 0
In [19]:
# converting Car_Brand and Car_Model to categorical variables with one-hot encoding

df6 = pd.get_dummies(df5, columns=['Car_Brand'])
df7 = pd.get_dummies(df6, columns=['Car_Model'])

Now we will do a Univariate & Bivariate analysis and treat Missing Values & Outliers

We will start by looking at the individual columns. (Univariate Analysis)

In [20]:
# installing pandas profiling library 
!pip install -U pandas_profiling
Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/
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In [21]:
# importing pandas profiler
from pandas_profiling import ProfileReport
In [22]:
# generating a pandas profile report to visualize the data / get a further understanding

df.profile_report()
Out[22]:

Obersvations about Columns:

  • Location mode is Mumbai. Top 5 most frequent values are Mumbai (count: 949), Hyderbad (count: 876), Coimbatore (count: 772), Kochi (count: 772), Pune (count: 765)
  • Year value ranges from 1996-2019, 2 modes; first at at 2015 and second at 2011. Year has a high correlation with Car_Age (understandably) and Car_Brand, so we will need to drop two of the columnns before modeling.
  • Kilometers_Driven ranges from 171 to 6,500,000, mean is 58699.06. We see that Kilometers_Driven is highly skewed (possibly due to the presence of outliers which we will check for) and has a high correlatiion with Car_Age so we will need to drop one of the columns before modelng.
  • Fuel_Type most to least frequent values: Diesel (count: 3852), Petrol (count: 3325), CNG (count: 62), LPG (count: 12), Electric (count: 2). Fuel type has a high correlation with Mileage andd Engine so we will need to remove two of the columns before modeling.
  • Transmission only has two distinct values: Manual (count: 5204) and Automatic (count: 2049). Transmission has a high correlation with Engine, Power, New_Price, Price, Car_Brand so some of the columns will need to be dropped before modeling.
  • Owner_Type most to least frequent values: First (count: 5952), Second (count: 1152), Third (count: 137), Fourt & Above (count: 12).
  • Mileage ranges from 0 to 33.54. There are 2 missing values in the columnn which will need to be treated and the Mileage column has a high correlation with Fuel_Type, Engine, Power, Price, Car_Brand so some of the columns will need to be dropped before modeling. Mileage has a mean of 18.14 and a median of 18.16 so the distribution is pretty normal.
  • Engine values range from 72 to 5998, with a mean of 1616.57 and median of 1493 so it is noticeably right-skewed. There are 46 missing values in the column which we will need to treat. The Engine column has a high correlation with Fuel_Type, Transmission, Mileage, Power, Seats, New_Price, Price, Car_Brand so some of the columns will need to be dropped before modeling.
  • Power values range from 34.2 to 616, with a mean of 112.765 and median of 94 so it is noticeably right-skewed. We see there are 175 missing values in the column we will need to treat. The Power column has a high correlation with Transmission, Mileage, Engine, New_Price, Price, Car_Brand so some of the columns will needd to be dropped before modeling.
  • Seats (ranging from 0-10) most frequent to least frequent values are: 5 seats (count: 6047), 7 seats (count: 796), 8 seats (count: 170), 4 seats (count: 119), 6 seats (count: 38), 2 seats (count: 18), 10 seats (count: 8), 9 seats (count: 3), 0 seats (count: 1). The Seats column has a high correlation with Engine and Car_Brand so two of the columns will need to be dropped before modeling. There are 53 missing values in the Seats column so they wll need to be treated. The Seats column has a mean of ~5.27972 and a median of 5.
  • New_Price ranges from 3.91 to 375, with a mean of ~21.30732 and median of 11.3 so it is noticeably right-skewed. The New_Price column has a high correlation with Transmission, Engine, Power, Price, and Car_Brand so some of the columns will need to be dropped before modeling.
  • Price ranges from 0.44 to 160, with a mean of ~9.479 and a median of 5.64 so it is noticeably right-skewed. There are 1234 missing values in the dataset which will need to be treated. The Price column has a high correlation with Transmission, Mileage, Engine, Power, New_Price, and Car_Brand so some of the columns will need to be dropped before modeling.
  • Car_Age ranges from 3 to 26, with a mean of 8.63 and a median of 8 so we can see it is slightly right-skewed. Car_Age has a high correlaton with the Year column (understandbly) so we will need to remove one of the columns before modeling.
  • Car_Model 10 most frequent values are: Maruti Swift Dzire (count: 189), Hyundai Grand i10 (count: 179), Maruti Wagon R (count: 178), Toyota Innova 2.5 (count: 145), Hyundai Verna 1.6 (count: 127), Honda City 1.5 (count: 122), Honda City i (count: 115), Hyundai Creta 1.6 (count: 110), Mercedes-Benz New C-Class (count: 110), BMW 3 Series (count: 109). The column has high cardinality (many unique values), this makes sense because there are many different Car models, even within each brand.
  • Car_Brand 10 most frequent values are: Maruti (count: 1444), Hyundai (count: 1340), Honda (count: 743), Toyota (count: 507), Mercedes-Benz (count: 380), Volkswagen (count: 374), Ford (count: 351), Mahindra (count: 331), BMW (count: 312), Audi (count: 285). Car_Brand has a high correlation with Year, Transmission, Mileage, Engine, Power, Seats, New_Price, Price so some of the columns will need to be dropped before modeling.

Illustrated Insights

Year Column

In [23]:
# Year Column

sns.histplot(data=df, x='Year', kde=True);

Kilometers_Driven Column

In [24]:
# Kilometers_Driven Column

sns.boxplot(data=df, x='Kilometers_Driven');
In [25]:
# Kilometers_Driven Column

sns.histplot(data=df, x='Kilometers_Driven', kde=False);

We see that the entire data is highly skewed and being affected by the presence of an outlier(s) which seems to be really far out. Let's see if the box-plot improves when we set show fliers to false.

In [26]:
# boxplot of Kilometers driven without outliers

sns.boxplot(data=df, x='Kilometers_Driven', showfliers = False);

We see this has helped significantly, it would be best to look more into the outlier(s) in this columnn so they don't negatively impact our model.

In [27]:
# Treating outlier(s) in Kilometers_Driven

# function to return lower and upper bounds of IQR
def outlier_treatment(column):
 sorted(column)
 Q1,Q3 = np.percentile(column , [25,75])
 IQR = Q3 - Q1
 lower_range = Q1 - (1.5 * IQR)
 upper_range = Q3 + (1.5 * IQR)
 return lower_range,upper_range

# using the function to return the lower and upper IQR bounds for Kilometers_Driven
lowerbound,upperbound = outlier_treatment(df['Kilometers_Driven'])

print(lowerbound, upperbound)
-24500.0 131500.0
In [28]:
# returning the rows where Kilometers_Driven contains outliers

df[(df['Kilometers_Driven'] < lowerbound) | (df['Kilometers_Driven'] > upperbound)]
Out[28]:
Location Year Kilometers_Driven Fuel_Type Transmission Owner_Type Mileage Engine Power Seats New_Price Price Car_Age Car_Model Car_Brand
29 Mumbai 2007-01-01 262000 Diesel Manual Fourth & Above 12.8 2494.0 102.00 7.0 24.010 4.00 15 Toyota Innova 2.5 Toyota
64 Chennai 2016-01-01 178000 Diesel Manual First 25.0 1396.0 69.00 5.0 7.630 2.50 6 Tata Indica V2 Tata
77 Chennai 2006-01-01 230000 Petrol Manual Third 12.4 1998.0 132.00 8.0 24.010 4.50 16 Toyota Innova 2.0 Toyota
154 Pune 2012-01-01 136997 Diesel Automatic First 17.2 1968.0 138.10 5.0 33.360 8.50 10 Skoda Superb Elegance Skoda
164 Kochi 2014-01-01 147898 Diesel Manual First 22.7 1498.0 89.84 5.0 11.685 4.42 8 Ford Ecosport 1.5 Ford
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
7183 Hyderabad 2009-01-01 137711 Diesel Manual First 16.8 1493.0 110.00 5.0 14.255 NaN 13 Hyundai Verna CRDi Hyundai
7198 Hyderabad 2012-01-01 147202 Diesel Automatic First 11.8 2993.0 241.60 7.0 120.000 NaN 10 Land Rover Discovery Land
7200 Pune 2015-01-01 190000 Diesel Manual First 23.2 1248.0 73.94 5.0 7.880 NaN 7 Maruti Ritz LDi Maruti
7213 Hyderabad 2013-01-01 170000 Diesel Manual First 22.3 1248.0 74.00 5.0 7.630 NaN 9 Tata Indica Vista Tata
7227 Chennai 2007-01-01 160000 Diesel Manual Second 19.2 1461.0 65.00 5.0 18.865 NaN 15 Mahindra Renault Logan Mahindra

258 rows × 15 columns

In [29]:
# function to set outliers to the lower and upper bounds of IQR respectively
def treat_outliers(df, col):
    Q1 = df[col].quantile(0.25)  # lower bound of IQR
    Q3 = df[col].quantile(0.75)  # upper bound of IQR
    IQR = Q3 - Q1                # IQR
    lower_whisker = Q1 - 1.5 * IQR
    upper_whisker = Q3 + 1.5 * IQR

    # all the values < lower bound will be set to the value of the lower bound
    # all the values > upper bound will be set to the value of the upper bound
    df[col] = np.clip(df[col], lower_whisker, upper_whisker)

    return df

# treating outliers using the function
df = treat_outliers(df,'Kilometers_Driven')

# visualizing the column after outlier treatment
sns.boxplot(data=df,x='Kilometers_Driven')
plt.show()

We see our box-plot looks normal without having to set fliers to false so this coluumn looks good now. Let's plot a histogram to double check our changes.

In [30]:
sns.histplot(data=df, x='Kilometers_Driven', kde=True);

We see a significant improvement from before.

Mileage Column

In [31]:
# Mileage Column
sns.histplot(data=df, x='Mileage', kde=True);

Engine Column

In [32]:
# Engine Column
sns.histplot(data=df, x='Engine', kde=True);

Power Column

In [33]:
# Power Column
sns.histplot(data=df, x='Power', kde=True);

Seats Column

In [34]:
# Seats Column
sns.histplot(data=df, x='Seats', kde=True);

New_Price Column

In [35]:
# New Price Column
sns.histplot(data=df, x='New_Price', kde=True);

Price Column

In [36]:
# Price Column
sns.histplot(data=df, x='Price', kde=True);

Car_Age Column

In [37]:
# Car_Age Column
sns.histplot(data=df, x='Car_Age', kde=True);

Car_Brand Column

In [38]:
# Car_Brand Column
sns.countplot(data=df, x='Car_Brand');
plt.xticks(rotation=90)
Out[38]:
(array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,
        17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32]),
 <a list of 33 Text major ticklabel objects>)
In [39]:
# calculating the number of unique Models for each Brand

Cargroups = df.groupby('Car_Brand')['Car_Model']
Cargroups.nunique()

Cargroups2 = Cargroups.nunique()
Cargroups2
Out[39]:
Car_Brand
Ambassador         1
Audi              31
BMW               20
Bentley            2
Chevrolet         22
Datsun             6
Fiat              17
Force              1
Ford              36
Hindustan          1
Honda             56
Hyundai           65
ISUZU              1
Isuzu              2
Jaguar            11
Jeep               2
Lamborghini        1
Land               3
Mahindra          56
Maruti           121
Mercedes-Benz     40
Mini               7
Mitsubishi         8
Nissan            19
OpelCorsa          1
Porsche            9
Renault           22
Skoda             39
Smart              1
Tata              46
Toyota            37
Volkswagen        33
Volvo             10
Name: Car_Model, dtype: int64

Location Column

In [40]:
# Location Column
sns.countplot(data=df, x='Location');
plt.xticks(rotation=90)
Out[40]:
(array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10]),
 <a list of 11 Text major ticklabel objects>)

Fuel_Type Column

In [41]:
# Fuel_Type Column
sns.countplot(data=df, x='Fuel_Type');
plt.xticks(rotation=90)
Out[41]:
(array([0, 1, 2, 3, 4]), <a list of 5 Text major ticklabel objects>)

Transmission Column

In [42]:
# Transmission Column
sns.countplot(data=df, x='Transmission');
plt.xticks(rotation=90)
Out[42]:
(array([0, 1]), <a list of 2 Text major ticklabel objects>)

Owner_Type Column

In [43]:
# Owner_Type Column
sns.countplot(data=df, x='Owner_Type');
plt.xticks(rotation=90)
Out[43]:
(array([0, 1, 2, 3]), <a list of 4 Text major ticklabel objects>)

Now we will do a Bivariate analysis and observe relationships between different columns in the dataset.

In [44]:
# creating a pair plot to observe relationships between the columns
sns.pairplot(data=df)
Out[44]:
<seaborn.axisgrid.PairGrid at 0x7fbe38a03ed0>
In [45]:
# creating a heatmat to observe relationships between the columns
sns.heatmap(data=df.corr(), annot=True);

The heatmap and pairplots confirm the observations made about correlations made from the Pandas Profile Report. This will help us later with feature extraction.

Relationship Between Car_Brand and Price

In [46]:
sns.scatterplot(data=df, x='Car_Brand', y='Price', hue="Location");
plt.xticks(rotation=90);

plt.legend(bbox_to_anchor=(1.5, 1), borderaxespad=0)
Out[46]:
<matplotlib.legend.Legend at 0x7fbe3c2e2e10>
In [47]:
sns.lineplot(data = df , x = 'Car_Brand' , y = 'Price');
plt.xticks(rotation=90);

Relationship between Price and Location

In [48]:
sns.scatterplot(data=df, x='Location', y='Price');
plt.xticks(rotation=90);
In [49]:
sns.lineplot(data = df , x = 'Location' , y = 'Price');
plt.xticks(rotation=90);

Relationship between Price and Transmission

In [50]:
sns.scatterplot(data=df, x='Transmission', y='Price');
plt.xticks(rotation=90);
In [51]:
sns.lineplot(data = df , x = 'Transmission' , y = 'Price');
plt.xticks(rotation=90);

Relationship between Price and Fuel_Type

In [52]:
sns.scatterplot(data=df, x='Fuel_Type', y='Price');
plt.xticks(rotation=90);
In [53]:
sns.lineplot(data = df , x = 'Fuel_Type' , y = 'Price');
plt.xticks(rotation=90);

Relationship between Price and Owner_Type

In [54]:
sns.scatterplot(data=df, x='Owner_Type', y='Price');
plt.xticks(rotation=90);
In [55]:
sns.lineplot(data = df , x = 'Owner_Type' , y = 'Price');
plt.xticks(rotation=90);

Treating Missing Values & Feature Extraction

In [56]:
# lets take another look at which columns having missing values again

df.isnull().sum().sort_values(ascending=False)
Out[56]:
Price                1234
Power                 175
Seats                  53
Engine                 46
Mileage                 2
Location                0
Year                    0
Kilometers_Driven       0
Fuel_Type               0
Transmission            0
Owner_Type              0
New_Price               0
Car_Age                 0
Car_Model               0
Car_Brand               0
dtype: int64
  • There are 5 columns in the dataset with missing values (Price; 1234, Power: 175, Seats: 53, Engine: 46, Mileage: 2).

Mileage Column

In [57]:
# extracting information of other variables where Mileage is null

df.loc[df['Mileage'].isnull()==True]
Out[57]:
Location Year Kilometers_Driven Fuel_Type Transmission Owner_Type Mileage Engine Power Seats New_Price Price Car_Age Car_Model Car_Brand
4446 Chennai 2016-01-01 50000.0 Electric Automatic First NaN 72.0 41.0 5.0 13.58 13.00 6 Mahindra E Verito Mahindra
4904 Mumbai 2011-01-01 44000.0 Electric Automatic First NaN 1798.0 73.0 5.0 24.01 12.75 11 Toyota Prius 2009-2016 Toyota

We see both rows with null values for Mileage have Automatic Transmission, Electric Fuel Type, and First Owner_Type. We can impute the missing values based off this information and using the Car_Model and Car_Brand. The distribution of Mileage is normal for the most part besides some larger values at the lower bound, so we will impute with the median.

In [58]:
df.groupby(['Car_Model','Car_Brand'])[['Transmission','Owner_Type','Fuel_Type','Mileage']].median()
Out[58]:
Mileage
Car_Model Car_Brand
Ambassador Classic Nova Ambassador 12.80
Audi A3 35 Audi 20.38
Audi A4 1.8 Audi 12.30
Audi A4 2.0 Audi 16.55
Audi A4 3.0 Audi 14.94
... ... ...
Volvo V40 Cross Volvo 23.30
Volvo V40 D3 Volvo 16.80
Volvo XC60 D4 Volvo 14.70
Volvo XC60 D5 Volvo 13.50
Volvo XC90 2007-2015 Volvo 11.10

727 rows × 1 columns

In [59]:
# df7['Mileage'] = df7['Mileage'].fillna(value = df7.groupby(['Car_Model','Car_Brand'])['Mileage'].transform('median'))
# df = df.groupby(['Car_Model','Car_Brand'])[['Transmission','Owner_Type','Fuel_Type','Mileage']].median()
# df7['Mileage'] = df7['Mileage'].fillna(value = df7.groupby(['Car_Brand','Car_Model'])['Mileage'].transform('mean'))
# df7['Mileage'] = df7['Mileage'].fillna(df7.groupby(['Car_Model', 'Car_Brand'])['Mileage'].transform('median'))

# Did not end up using this code ^
# Kept getting errors trying to impute with GroupBy so I decided to impute values based on the mean or median of the columns
In [60]:
# The Mileage column is normally distributed so we will impute missng values with the mean
# Checking the mean of the Mileage column

df7['Mileage'].mean()
Out[60]:
18.141580471659083
In [61]:
# Imputing the missing values in Mileage with the mean

df7['Mileage'].fillna(value=df7['Mileage'].mean(), inplace=True)

Engine Column

In [62]:
# The Engine column's distribution is skewed so we will impute missing values with the median
# Checking the median of the Engine column

df7['Engine'].median()
Out[62]:
1493.0
In [63]:
# Imputing the missing values in Engine with the median

df7['Engine'].fillna(value=df7['Engine'].median(), inplace=True)

Seats Column

In [64]:
# The Seats column is normally distributed so we will impute missing values with the mean
# Checking the mean of the Seats column

df7['Seats'].mean()
Out[64]:
5.279722222222222
In [65]:
# Imputing the missing values in Seats with the mean

df7['Seats'].fillna(value=df7['Seats'].mean(), inplace=True)

Power Column

In [66]:
# The Power column's distribution is skewed so we will impute missing values with the median
# Checking the median of the Engine column

df7['Power'].median()
Out[66]:
94.0
In [67]:
# Imputing the missing values in Power with the median

df7['Power'].fillna(value=df7['Power'].median(), inplace=True)

Price Column

In [68]:
# The Price column's distribution is skewed so we will impute missing values with the median
# Checking the median of the Price column

df7['Price'].median()
Out[68]:
5.64
In [69]:
# Imputing the missing values in Price with the median

df7['Price'].fillna(value=df7['Price'].median(), inplace=True)
In [70]:
# Checking that the values have been imputed correctly

df7.isnull().sum().sort_values(ascending=False)
Out[70]:
Year                                           0
Car_Model_Mercedes-Benz B Class                0
Car_Model_Mercedes-Benz CLA 200                0
Car_Model_Mercedes-Benz CLA 45                 0
Car_Model_Mercedes-Benz CLS-Class 2006-2010    0
                                              ..
Car_Model_Hyundai EON Era                      0
Car_Model_Hyundai EON LPG                      0
Car_Model_Hyundai EON Magna                    0
Car_Model_Hyundai EON Sportz                   0
Car_Model_Volvo XC90 2007-2015                 0
Length: 791, dtype: int64

Everything looks good so we will now move onto feature extraction.

In [71]:
# first we will drop the Year column as it is highly correlated with Car_Age
df7.drop(['Year'], axis=1, inplace=True)
In [72]:
# next we will drop the Power column as it is highly correlated with Engine, New_Price, Price
df7.drop(['Power'], axis=1, inplace=True)

Building the Model

In [73]:
# dropping Price from X to prepare for modelling

X = df7.drop("Price", axis=1)
y = df7["Price"]
In [74]:
# splitting the dataset into training set (70%) and test set (30%)

X_train, X_test, y_train, y_test = train_test_split( X, y, test_size=0.30, random_state=1)
In [75]:
# fitting the model to the training set

regression_model = LinearRegression()
regression_model.fit(X_train, y_train)
Out[75]:
LinearRegression()
In [76]:
# calculating the R-squared value of the training set

print(regression_model.score(X_train, y_train),)
0.7915323602146134
In [77]:
# calculating the RMSE value of the training set

print(np.sqrt(mean_squared_error(y_train, regression_model.predict(X_train))))
4.585773316936907
In [78]:
# calculating the R-squared value of the test set

print(regression_model.score(X_test, y_test),)
0.6342641745975273
In [79]:
# calculating the RMSE value of thee test set

print(np.sqrt(mean_squared_error(y_test, regression_model.predict(X_test))))
6.560592191648514
In [80]:
# taking a look at the coefficients and intercepts 

equationinfo = pd.DataFrame(
    np.append(regression_model.coef_, regression_model.intercept_),
    index=X_train.columns.tolist() + ["Intercept"],
    columns=["Coefficients"],
)

equationinfo
Out[80]:
Coefficients
Kilometers_Driven -0.000013
Mileage -0.012015
Engine 0.003753
Seats 0.652514
New_Price 0.070281
... ...
Car_Model_Volvo V40 D3 4.308275
Car_Model_Volvo XC60 D4 -2.685279
Car_Model_Volvo XC60 D5 4.078033
Car_Model_Volvo XC90 2007-2015 0.000000
Intercept 10.332540

789 rows × 1 columns

In [81]:
# exporting the linear regression equation 

Equation = "Price = " + str(regression_model.intercept_)
print(Equation, end=" ")

for i in range(len(X_train.columns)):
    if i != len(X_train.columns) - 1:
        print(
            "+ (",
            regression_model.coef_[i],
            ")*(",
            X_train.columns[i],
            ")",
            end="  ",
        )
    else:
        print("+ (", regression_model.coef_[i], ")*(", X_train.columns[i], ")")
Price = 10.332540194369846 + ( -1.2966493823937262e-05 )*( Kilometers_Driven )  + ( -0.012014511830110446 )*( Mileage )  + ( 0.003753199262574114 )*( Engine )  + ( 0.6525141663707462 )*( Seats )  + ( 0.0702808236676411 )*( New_Price )  + ( -0.8563640412258009 )*( Car_Age )  + ( -0.23818374015061727 )*( Location_Ahmedabad )  + ( 1.2265972875475875 )*( Location_Bangalore )  + ( 0.485265132109892 )*( Location_Chennai )  + ( 0.8304205118725987 )*( Location_Coimbatore )  + ( -0.8235573127460829 )*( Location_Delhi )  + ( 0.637842905219689 )*( Location_Hyderabad )  + ( 0.35403877636896663 )*( Location_Jaipur )  + ( -0.6673569382167641 )*( Location_Kochi )  + ( -1.1539883026376736 )*( Location_Kolkata )  + ( -0.5091239407488892 )*( Location_Mumbai )  + ( -0.14195425125921113 )*( Location_Pune )  + ( 0.00243742168071015 )*( Fuel_Type_CNG )  + ( 0.7471548817050984 )*( Fuel_Type_Diesel )  + ( 1.0728098409629183e-08 )*( Fuel_Type_Electric )  + ( -0.9561338263687322 )*( Fuel_Type_LPG )  + ( 0.20654153790189067 )*( Fuel_Type_Petrol )  + ( 0.20957614452167395 )*( Transmission_Automatic )  + ( -0.2095761617628069 )*( Transmission_Manual )  + ( -0.2808400670887572 )*( Owner_Type_First )  + ( 0.9061157053307856 )*( Owner_Type_Fourth & Above )  + ( -0.43611211817257267 )*( Owner_Type_Second )  + ( -0.18916358536097722 )*( Owner_Type_Third )  + ( -0.9997281308162997 )*( Car_Brand_Ambassador )  + ( 5.028978838325591 )*( Car_Brand_Audi )  + ( 9.580147839977466 )*( Car_Brand_BMW )  + ( -11.555895664062797 )*( Car_Brand_Bentley )  + ( -6.2528429277751 )*( Car_Brand_Chevrolet )  + ( -7.6963076371822625 )*( Car_Brand_Datsun )  + ( -5.163876304906783 )*( Car_Brand_Fiat )  + ( -3.114296807182033 )*( Car_Brand_Force )  + ( -5.563056459361126 )*( Car_Brand_Ford )  + ( 3.540631285743566 )*( Car_Brand_Hindustan )  + ( -6.159797756364839 )*( Car_Brand_Honda )  + ( -4.685830089694876 )*( Car_Brand_Hyundai )  + ( -4.924141122932953 )*( Car_Brand_ISUZU )  + ( -8.181075400459648e-10 )*( Car_Brand_Isuzu )  + ( 11.909084040412766 )*( Car_Brand_Jaguar )  + ( 0.30548899149384023 )*( Car_Brand_Jeep )  + ( 49.10719597770826 )*( Car_Brand_Lamborghini )  + ( 5.131095032417614 )*( Car_Brand_Land )  + ( -8.585455847536869 )*( Car_Brand_Mahindra )  + ( -4.978629425475278 )*( Car_Brand_Maruti )  + ( 7.638006271139879 )*( Car_Brand_Mercedes-Benz )  + ( 4.92474172964285 )*( Car_Brand_Mini )  + ( -4.819326721325352 )*( Car_Brand_Mitsubishi )  + ( -5.963258663004147 )*( Car_Brand_Nissan )  + ( 0.6079725930581077 )*( Car_Brand_OpelCorsa )  + ( 13.535876868774574 )*( Car_Brand_Porsche )  + ( -6.916403682741803 )*( Car_Brand_Renault )  + ( -5.7179857594749715 )*( Car_Brand_Skoda )  + ( 0.25771636857378855 )*( Car_Brand_Smart )  + ( -6.925431582655376 )*( Car_Brand_Tata )  + ( -4.936900984148645 )*( Car_Brand_Toyota )  + ( -5.4714635242846414 )*( Car_Brand_Volkswagen )  + ( -1.136306737939214 )*( Car_Brand_Volvo )  + ( -0.9997281309132913 )*( Car_Model_Ambassador Classic Nova )  + ( -10.819921368253004 )*( Car_Model_Audi A3 35 )  + ( -8.761035291442983 )*( Car_Model_Audi A4 1.8 )  + ( -5.51722942742963 )*( Car_Model_Audi A4 2.0 )  + ( -10.863707108347441 )*( Car_Model_Audi A4 3.0 )  + ( -5.142454240214533e-10 )*( Car_Model_Audi A4 3.2 )  + ( 0.377813659043459 )*( Car_Model_Audi A4 30 )  + ( -1.2249540171203843 )*( Car_Model_Audi A4 35 )  + ( -7.655935909717413 )*( Car_Model_Audi A4 New )  + ( -4.339546520724293 )*( Car_Model_Audi A6 2.0 )  + ( -11.696744435668068 )*( Car_Model_Audi A6 2.7 )  + ( -1.1995640036843724e-09 )*( Car_Model_Audi A6 2.8 )  + ( -7.184153687350344 )*( Car_Model_Audi A6 2011-2015 )  + ( -9.161218139301848 )*( Car_Model_Audi A6 3.0 )  + ( 4.38101856282536 )*( Car_Model_Audi A6 35 )  + ( 4.624635294526964 )*( Car_Model_Audi A7 2011-2015 )  + ( -2.6573310066706175 )*( Car_Model_Audi A8 L )  + ( -0.7063943639047375 )*( Car_Model_Audi Q3 2.0 )  + ( -6.365499241477636 )*( Car_Model_Audi Q3 2012-2015 )  + ( -9.192823819809588 )*( Car_Model_Audi Q3 30 )  + ( -3.1231161473403817 )*( Car_Model_Audi Q3 35 )  + ( 1.1716640753093392 )*( Car_Model_Audi Q5 2.0 )  + ( -5.261672474219514 )*( Car_Model_Audi Q5 2008-2012 )  + ( 8.247278244751305 )*( Car_Model_Audi Q5 3.0 )  + ( 14.618949140961684 )*( Car_Model_Audi Q5 30 )  + ( 1.5262213082906082 )*( Car_Model_Audi Q7 3.0 )  + ( 13.84751617447872 )*( Car_Model_Audi Q7 35 )  + ( 2.0295463261008977 )*( Car_Model_Audi Q7 4.2 )  + ( 33.608125121331064 )*( Car_Model_Audi Q7 45 )  + ( 9.434855389923298 )*( Car_Model_Audi RS5 Coupe )  + ( 1.0937148964274002e-09 )*( Car_Model_Audi TT 2.0 )  + ( 15.692638505769104 )*( Car_Model_Audi TT 40 )  + ( -11.489986826523618 )*( Car_Model_BMW 1 Series )  + ( -10.33271813461674 )*( Car_Model_BMW 3 Series )  + ( -7.4991045909390355 )*( Car_Model_BMW 5 Series )  + ( 3.8982825931329557 )*( Car_Model_BMW 6 Series )  + ( -4.3385006515757185 )*( Car_Model_BMW 7 Series )  + ( 0.9488223584328349 )*( Car_Model_BMW X1 M )  + ( -6.443738927704767 )*( Car_Model_BMW X1 sDrive )  + ( -13.889681620449677 )*( Car_Model_BMW X1 sDrive20d )  + ( -8.440215829225728 )*( Car_Model_BMW X1 xDrive )  + ( -20.69589799293606 )*( Car_Model_BMW X3 2.5si )  + ( 9.870969778034375 )*( Car_Model_BMW X3 xDrive )  + ( -3.895984251721903 )*( Car_Model_BMW X3 xDrive20d )  + ( 9.944706999893269 )*( Car_Model_BMW X3 xDrive30d )  + ( 13.5843955925124 )*( Car_Model_BMW X5 2014-2019 )  + ( -8.919750093301646 )*( Car_Model_BMW X5 3.0d )  + ( 26.74816609020725 )*( Car_Model_BMW X5 X5 )  + ( -0.622637689793044 )*( Car_Model_BMW X5 xDrive )  + ( 32.530996389533804 )*( Car_Model_BMW X6 xDrive )  + ( -6.763216174234346 )*( Car_Model_BMW X6 xDrive30d )  + ( 15.385240823070506 )*( Car_Model_BMW Z4 2009-2013 )  + ( 22.400721395036047 )*( Car_Model_Bentley Continental Flying )  + ( -33.95661706196423 )*( Car_Model_Bentley Flying Spur )  + ( 2.616725585791474 )*( Car_Model_Chevrolet Aveo 1.4 )  + ( 2.183984305949061 )*( Car_Model_Chevrolet Aveo 1.6 )  + ( 3.186099930119858 )*( Car_Model_Chevrolet Aveo U-VA )  + ( -0.3258323217872467 )*( Car_Model_Chevrolet Beat Diesel )  + ( 0.8499073216707387 )*( Car_Model_Chevrolet Beat LS )  + ( 1.028420620865985 )*( Car_Model_Chevrolet Beat LT )  + ( 1.4102734212013717 )*( Car_Model_Chevrolet Beat Option )  + ( 0.11709905593748471 )*( Car_Model_Chevrolet Captiva LT )  + ( 4.412308296508627e-10 )*( Car_Model_Chevrolet Captiva LTZ )  + ( -1.3411003706377302 )*( Car_Model_Chevrolet Cruze LTZ )  + ( -2.520207168964178 )*( Car_Model_Chevrolet Enjoy 1.3 )  + ( -3.0270903579024484 )*( Car_Model_Chevrolet Enjoy 1.4 )  + ( -3.300288844437879 )*( Car_Model_Chevrolet Enjoy Petrol )  + ( -2.9344237155957917 )*( Car_Model_Chevrolet Enjoy TCDi )  + ( -0.9295352066150436 )*( Car_Model_Chevrolet Optra 1.6 )  + ( -0.2631264487603989 )*( Car_Model_Chevrolet Optra Magnum )  + ( -0.49648867305973576 )*( Car_Model_Chevrolet Sail 1.2 )  + ( -0.046032705761173326 )*( Car_Model_Chevrolet Sail Hatchback )  + ( -1.7432591729249352 )*( Car_Model_Chevrolet Sail LT )  + ( 3.3598502062861435 )*( Car_Model_Chevrolet Spark 1.0 )  + ( 0.8034108472643661 )*( Car_Model_Chevrolet Tavera LS )  + ( -4.881229232754831 )*( Car_Model_Chevrolet Tavera LT )  + ( -1.2217921419572821 )*( Car_Model_Datsun GO NXT )  + ( -2.3329459693930152 )*( Car_Model_Datsun GO Plus )  + ( 0.014543635516768474 )*( Car_Model_Datsun GO T )  + ( -2.42621708711474 )*( Car_Model_Datsun Redi GO )  + ( -0.9092025925354715 )*( Car_Model_Datsun redi-GO S )  + ( -0.8206934818951501 )*( Car_Model_Datsun redi-GO T )  + ( -2.7744179318860436 )*( Car_Model_Fiat Abarth 595 )  + ( -4.346150106471214e-10 )*( Car_Model_Fiat Avventura FIRE )  + ( -1.2114327517521515 )*( Car_Model_Fiat Avventura MULTIJET )  + ( -4.400422068279367 )*( Car_Model_Fiat Avventura Urban )  + ( 0.944980984632467 )*( Car_Model_Fiat Grande Punto )  + ( -0.043383171560881006 )*( Car_Model_Fiat Linea 1.3 )  + ( 0.6902445576350019 )*( Car_Model_Fiat Linea Classic )  + ( 3.2778279275689406 )*( Car_Model_Fiat Linea Dynamic )  + ( 0.07138249339103975 )*( Car_Model_Fiat Linea Emotion )  + ( -6.706912802911802e-11 )*( Car_Model_Fiat Linea T )  + ( -2.3344939052197584 )*( Car_Model_Fiat Linea T-Jet )  + ( 4.000987206183876 )*( Car_Model_Fiat Petra 1.2 )  + ( 2.260638343187793e-09 )*( Car_Model_Fiat Punto 1.2 )  + ( -2.085578232786755 )*( Car_Model_Fiat Punto 1.3 )  + ( 0.5592162192488793 )*( Car_Model_Fiat Punto 1.4 )  + ( -1.8587876342742276 )*( Car_Model_Fiat Punto EVO )  + ( -5.667288860422559e-11 )*( Car_Model_Fiat Siena 1.2 )  + ( -3.1142968042239705 )*( Car_Model_Force One LX )  + ( -3.6789551205533915 )*( Car_Model_Ford Aspire Ambiente )  + ( -0.3362962451408282 )*( Car_Model_Ford Aspire Titanium )  + ( 1.274461425282425e-09 )*( Car_Model_Ford Classic 1.4 )  + ( 1.273497637154992 )*( Car_Model_Ford EcoSport 1.0 )  + ( -0.8604497102206549 )*( Car_Model_Ford EcoSport 1.5 )  + ( 0.4188800991789643 )*( Car_Model_Ford Ecosport 1.0 )  + ( -0.4244515804986153 )*( Car_Model_Ford Ecosport 1.5 )  + ( -0.27600445875173696 )*( Car_Model_Ford Ecosport Signature )  + ( 6.858301734125215 )*( Car_Model_Ford Endeavour 2.2 )  + ( -4.895026682124409 )*( Car_Model_Ford Endeavour 2.5L )  + ( -5.8568099242583065 )*( Car_Model_Ford Endeavour 3.0L )  + ( 10.239755165718217 )*( Car_Model_Ford Endeavour 3.2 )  + ( -5.354875056779456 )*( Car_Model_Ford Endeavour 4x2 )  + ( -4.9548579006206515 )*( Car_Model_Ford Endeavour Hurricane )  + ( 9.116664154257125 )*( Car_Model_Ford Endeavour Titanium )  + ( 1.4845508156113851e-09 )*( Car_Model_Ford Endeavour XLT )  + ( 2.1196228225983087 )*( Car_Model_Ford Fiesta 1.4 )  + ( -0.9764793330941955 )*( Car_Model_Ford Fiesta 1.5 )  + ( 0.7986533692851927 )*( Car_Model_Ford Fiesta 1.6 )  + ( -0.8673845478883815 )*( Car_Model_Ford Fiesta Classic )  + ( 0.28451192213905907 )*( Car_Model_Ford Fiesta Diesel )  + ( -0.8971909278919494 )*( Car_Model_Ford Fiesta EXi )  + ( 2.2115731468375088e-10 )*( Car_Model_Ford Fiesta Titanium )  + ( -1.7927459826371632 )*( Car_Model_Ford Figo 1.2P )  + ( -1.4759870134034183 )*( Car_Model_Ford Figo 1.5D )  + ( -3.1687227891063547 )*( Car_Model_Ford Figo 2015-2019 )  + ( -2.3291780740923 )*( Car_Model_Ford Figo Aspire )  + ( -1.375194925313215 )*( Car_Model_Ford Figo Diesel )  + ( 0.36495604393186754 )*( Car_Model_Ford Figo Petrol )  + ( -3.559785990985845 )*( Car_Model_Ford Figo Titanium )  + ( -2.2753070865105562 )*( Car_Model_Ford Freestyle Titanium )  + ( 1.330919205927043 )*( Car_Model_Ford Fusion Plus )  + ( 3.097693289689226 )*( Car_Model_Ford Ikon 1.3 )  + ( 0.17580087956558177 )*( Car_Model_Ford Ikon 1.4 )  + ( 3.71339056523202 )*( Car_Model_Ford Ikon 1.6 )  + ( -1.7059593737656087e-09 )*( Car_Model_Ford Mustang V8 )  + ( 3.540631288237809 )*( Car_Model_Hindustan Motors Contessa )  + ( -0.3884869385388867 )*( Car_Model_Honda Accord 2.4 )  + ( 0.1214215444132789 )*( Car_Model_Honda Accord 2001-2003 )  + ( -2.087401215862746 )*( Car_Model_Honda Accord V6 )  + ( -0.7915662290735486 )*( Car_Model_Honda Accord VTi-L )  + ( -0.45771926967750204 )*( Car_Model_Honda Amaze E )  + ( -0.6743487366870082 )*( Car_Model_Honda Amaze EX )  + ( -0.6345587843298862 )*( Car_Model_Honda Amaze S )  + ( -1.0336384805155698 )*( Car_Model_Honda Amaze SX )  + ( -0.8806690254803643 )*( Car_Model_Honda Amaze V )  + ( -0.6324288145351358 )*( Car_Model_Honda Amaze VX )  + ( -3.60795393561375e-10 )*( Car_Model_Honda BR-V i-DTEC )  + ( -2.0764143316639987 )*( Car_Model_Honda BR-V i-VTEC )  + ( -5.610658300951069 )*( Car_Model_Honda BRV i-DTEC )  + ( -1.8000937700313013 )*( Car_Model_Honda BRV i-VTEC )  + ( 0.9508631037975505 )*( Car_Model_Honda Brio 1.2 )  + ( 0.03365545530497324 )*( Car_Model_Honda Brio E )  + ( -0.8903235028328774 )*( Car_Model_Honda Brio EX )  + ( 0.017837815837687954 )*( Car_Model_Honda Brio S )  + ( 0.7642504318122803 )*( Car_Model_Honda Brio V )  + ( -0.3840010183831183 )*( Car_Model_Honda Brio VX )  + ( 3.051392485729052 )*( Car_Model_Honda CR-V 2.0 )  + ( 1.7290989909701242 )*( Car_Model_Honda CR-V 2.0L )  + ( 0.14036411795983456 )*( Car_Model_Honda CR-V 2.4 )  + ( 2.4510063848912145 )*( Car_Model_Honda CR-V 2.4L )  + ( 8.211014090875324e-10 )*( Car_Model_Honda CR-V AT )  + ( -3.8285311016461847 )*( Car_Model_Honda CR-V Diesel )  + ( 2.3402565504543893 )*( Car_Model_Honda CR-V Petrol )  + ( 1.87562922632911 )*( Car_Model_Honda CR-V RVi )  + ( 1.8052859624865576 )*( Car_Model_Honda CR-V Sport )  + ( 5.1055574694732835 )*( Car_Model_Honda City 1.3 )  + ( 1.580579180490433 )*( Car_Model_Honda City 1.5 )  + ( 0.4734370505152246 )*( Car_Model_Honda City Corporate )  + ( 0.9862505501711831 )*( Car_Model_Honda City V )  + ( 3.4043938160096 )*( Car_Model_Honda City ZX )  + ( 0.1625011259365423 )*( Car_Model_Honda City i )  + ( 1.230788043799565 )*( Car_Model_Honda City i-DTEC )  + ( 0.4163566974416002 )*( Car_Model_Honda City i-VTEC )  + ( 1.5574841919557372 )*( Car_Model_Honda Civic 2006-2010 )  + ( -0.4671738974198854 )*( Car_Model_Honda Civic 2010-2013 )  + ( -0.6299391327764408 )*( Car_Model_Honda Jazz 1.2 )  + ( -1.6283598928416194 )*( Car_Model_Honda Jazz 1.5 )  + ( -3.482219852026014 )*( Car_Model_Honda Jazz 2020 )  + ( 1.8061882383399193 )*( Car_Model_Honda Jazz Active )  + ( -0.3786571939188616 )*( Car_Model_Honda Jazz Exclusive )  + ( 1.7184145821806591 )*( Car_Model_Honda Jazz Mode )  + ( 2.3067433324411106 )*( Car_Model_Honda Jazz S )  + ( 1.2265789567378205 )*( Car_Model_Honda Jazz Select )  + ( 0.3774964168923935 )*( Car_Model_Honda Jazz V )  + ( -1.6791740000822635 )*( Car_Model_Honda Jazz VX )  + ( -2.0368721996658152 )*( Car_Model_Honda Mobilio E )  + ( -2.938891766090332 )*( Car_Model_Honda Mobilio RS )  + ( -2.5563951512162193 )*( Car_Model_Honda Mobilio S )  + ( -1.8207031092444768 )*( Car_Model_Honda Mobilio V )  + ( -3.311129148642067e-11 )*( Car_Model_Honda WR-V Edge )  + ( -4.783224598004899 )*( Car_Model_Honda WRV i-DTEC )  + ( 0.7788208259683813 )*( Car_Model_Honda WRV i-VTEC )  + ( 4.008080950139071 )*( Car_Model_Hyundai Accent CRDi )  + ( 0.47023157585386594 )*( Car_Model_Hyundai Accent Executive )  + ( 1.1562575719549728 )*( Car_Model_Hyundai Accent GLE )  + ( -0.011680826843507297 )*( Car_Model_Hyundai Accent GLS )  + ( 3.049722438524117 )*( Car_Model_Hyundai Accent GLX )  + ( -1.1911865032735154 )*( Car_Model_Hyundai Creta 1.4 )  + ( 0.16855559449866797 )*( Car_Model_Hyundai Creta 1.6 )  + ( -1.6651680603345662 )*( Car_Model_Hyundai EON 1.0 )  + ( -1.7829200610319038 )*( Car_Model_Hyundai EON D )  + ( -1.611057962970136 )*( Car_Model_Hyundai EON Era )  + ( -7.325473561081708e-10 )*( Car_Model_Hyundai EON LPG )  + ( -2.4208482427585944 )*( Car_Model_Hyundai EON Magna )  + ( -3.2307250212535075 )*( Car_Model_Hyundai EON Sportz )  + ( -0.0667732907086741 )*( Car_Model_Hyundai Elantra 1.6 )  + ( -0.6315256407337378 )*( Car_Model_Hyundai Elantra 2.0 )  + ( 0.09764014710268398 )*( Car_Model_Hyundai Elantra CRDi )  + ( 3.108809412270218 )*( Car_Model_Hyundai Elantra GT )  + ( -4.818001430417386 )*( Car_Model_Hyundai Elantra SX )  + ( -1.6964596200169324 )*( Car_Model_Hyundai Elite i20 )  + ( 1.8886767065405734 )*( Car_Model_Hyundai Getz 1.3 )  + ( -1.2881056960389452 )*( Car_Model_Hyundai Getz 1.5 )  + ( 1.6851352284845644 )*( Car_Model_Hyundai Getz GLE )  + ( 2.0869065762918404 )*( Car_Model_Hyundai Getz GLS )  + ( 5.48035166311323 )*( Car_Model_Hyundai Getz GVS )  + ( -2.5995124012121087 )*( Car_Model_Hyundai Grand i10 )  + ( 1.458860021829217 )*( Car_Model_Hyundai Santa Fe )  + ( 3.2159827505775067 )*( Car_Model_Hyundai Santro AT )  + ( 7.144748584926705 )*( Car_Model_Hyundai Santro D )  + ( 1.4426531080857785e-09 )*( Car_Model_Hyundai Santro DX )  + ( -0.6096991697200185 )*( Car_Model_Hyundai Santro GLS )  + ( 4.519071078008138 )*( Car_Model_Hyundai Santro GS )  + ( 2.459049812857202 )*( Car_Model_Hyundai Santro LP )  + ( 5.086835386121129 )*( Car_Model_Hyundai Santro LS )  + ( 2.2711616706290156 )*( Car_Model_Hyundai Santro Xing )  + ( -1.6300596265574925 )*( Car_Model_Hyundai Sonata 2.4 )  + ( 2.0325641079211985 )*( Car_Model_Hyundai Sonata Embera )  + ( 1.3136889028262773 )*( Car_Model_Hyundai Sonata GOLD )  + ( -0.424848068020175 )*( Car_Model_Hyundai Sonata Transform )  + ( -1.5643558036846315 )*( Car_Model_Hyundai Tucson 2.0 )  + ( -1.6428759743820718 )*( Car_Model_Hyundai Tucson CRDi )  + ( -1.8091254339780165 )*( Car_Model_Hyundai Verna 1.4 )  + ( -1.4894463885196756 )*( Car_Model_Hyundai Verna 1.6 )  + ( -0.29859597302512975 )*( Car_Model_Hyundai Verna CRDi )  + ( -0.7794100206872541 )*( Car_Model_Hyundai Verna SX )  + ( -1.6550730536050315 )*( Car_Model_Hyundai Verna Transform )  + ( -1.3840354893620859 )*( Car_Model_Hyundai Verna VTVT )  + ( -1.4511251867474533 )*( Car_Model_Hyundai Verna XXi )  + ( -0.8778879074882406 )*( Car_Model_Hyundai Verna Xi )  + ( -1.9261817168808413 )*( Car_Model_Hyundai Xcent 1.1 )  + ( -2.5270124054205567 )*( Car_Model_Hyundai Xcent 1.2 )  + ( -0.7854091680684618 )*( Car_Model_Hyundai i10 Asta )  + ( 0.4487258518701099 )*( Car_Model_Hyundai i10 Era )  + ( 0.18037480611343515 )*( Car_Model_Hyundai i10 Magna )  + ( 1.938517561581404 )*( Car_Model_Hyundai i10 Magna(O) )  + ( -0.8941708933307706 )*( Car_Model_Hyundai i10 Sportz )  + ( -1.0219057090638117 )*( Car_Model_Hyundai i20 1.2 )  + ( -1.657304073439195 )*( Car_Model_Hyundai i20 1.4 )  + ( -0.1299279743568765 )*( Car_Model_Hyundai i20 2015-2017 )  + ( -2.0850645275158115 )*( Car_Model_Hyundai i20 Active )  + ( -1.124735907304241 )*( Car_Model_Hyundai i20 Asta )  + ( -3.1356853810419425 )*( Car_Model_Hyundai i20 Diesel )  + ( -2.332990086008443 )*( Car_Model_Hyundai i20 Era )  + ( -0.8505939417644379 )*( Car_Model_Hyundai i20 Magna )  + ( -1.3678971300492329 )*( Car_Model_Hyundai i20 Sportz )  + ( -1.486396712061448 )*( Car_Model_Hyundai i20 new )  + ( -4.924141125607768 )*( Car_Model_ISUZU D-MAX V-Cross )  + ( -2.403754528756963e-09 )*( Car_Model_Isuzu MU 7 )  + ( -1.2179075525864391e-09 )*( Car_Model_Isuzu MUX 4WD )  + ( -6.63165966585666e-10 )*( Car_Model_Jaguar F Type )  + ( -27.722787178475684 )*( Car_Model_Jaguar XE 2.0L )  + ( -28.558491323344843 )*( Car_Model_Jaguar XE Portfolio )  + ( 17.360583801705204 )*( Car_Model_Jaguar XF 2.0 )  + ( -3.6643394334625246 )*( Car_Model_Jaguar XF 2.2 )  + ( -11.732639100934142 )*( Car_Model_Jaguar XF 3.0 )  + ( 2.536095431237909 )*( Car_Model_Jaguar XF Aero )  + ( -11.408287567458958 )*( Car_Model_Jaguar XF Diesel )  + ( 42.00146774724584 )*( Car_Model_Jaguar XJ 2.0L )  + ( 33.0974816741103 )*( Car_Model_Jaguar XJ 3.0L )  + ( -4.311928591960168e-10 )*( Car_Model_Jaguar XJ 5.0 )  + ( 1.9505381847219032 )*( Car_Model_Jeep Compass 1.4 )  + ( -1.645049193593925 )*( Car_Model_Jeep Compass 2.0 )  + ( 49.10719597987951 )*( Car_Model_Lamborghini Gallardo Coupe )  + ( 2.484999364724861 )*( Car_Model_Land Rover Discovery )  + ( -10.227221777869973 )*( Car_Model_Land Rover Freelander )  + ( 12.87331744257056 )*( Car_Model_Land Rover Range )  + ( -0.11796673521106493 )*( Car_Model_Mahindra Bolero DI )  + ( -4.224055236644966 )*( Car_Model_Mahindra Bolero Power )  + ( -1.7437705269781945 )*( Car_Model_Mahindra Bolero SLE )  + ( -7.921243661002109e-10 )*( Car_Model_Mahindra Bolero SLX )  + ( -2.0450001035028906 )*( Car_Model_Mahindra Bolero VLX )  + ( -2.620282596537501 )*( Car_Model_Mahindra Bolero ZLX )  + ( -0.5842463814918484 )*( Car_Model_Mahindra Bolero mHAWK )  + ( -2.489475292577481e-10 )*( Car_Model_Mahindra E Verito )  + ( 4.14299015070668 )*( Car_Model_Mahindra Jeep MM )  + ( 0.2529953857968583 )*( Car_Model_Mahindra KUV 100 )  + ( 0.6577869840355637 )*( Car_Model_Mahindra Logan Diesel )  + ( 2.411428994994772 )*( Car_Model_Mahindra Logan Petrol )  + ( -2.180393543772909 )*( Car_Model_Mahindra NuvoSport N6 )  + ( -1.3088286010543015e-10 )*( Car_Model_Mahindra NuvoSport N8 )  + ( -1.7264260383950811 )*( Car_Model_Mahindra Quanto C2 )  + ( -0.4006093441127273 )*( Car_Model_Mahindra Quanto C4 )  + ( 1.192646648107143e-09 )*( Car_Model_Mahindra Quanto C6 )  + ( -0.814544142922579 )*( Car_Model_Mahindra Quanto C8 )  + ( 5.827077593850506 )*( Car_Model_Mahindra Renault Logan )  + ( 1.6438617314078923 )*( Car_Model_Mahindra Scorpio 1.99 )  + ( -0.151167034858603 )*( Car_Model_Mahindra Scorpio 2.6 )  + ( -0.6280668856996456 )*( Car_Model_Mahindra Scorpio 2009-2014 )  + ( 0.43693778849067527 )*( Car_Model_Mahindra Scorpio DX )  + ( -0.9133500405822883 )*( Car_Model_Mahindra Scorpio LX )  + ( 0.535888985607392 )*( Car_Model_Mahindra Scorpio S10 )  + ( -5.641094258379553e-10 )*( Car_Model_Mahindra Scorpio S2 )  + ( -2.2382141500621597 )*( Car_Model_Mahindra Scorpio S4 )  + ( -0.2859362779905154 )*( Car_Model_Mahindra Scorpio S6 )  + ( 0.5660799440147386 )*( Car_Model_Mahindra Scorpio S8 )  + ( -1.2518792773844756 )*( Car_Model_Mahindra Scorpio SLE )  + ( 0.6630132046370187 )*( Car_Model_Mahindra Scorpio SLX )  + ( 1.7016525073842876 )*( Car_Model_Mahindra Scorpio VLS )  + ( -0.2607339354459874 )*( Car_Model_Mahindra Scorpio VLX )  + ( 0.6353212057903947 )*( Car_Model_Mahindra Ssangyong Rexton )  + ( -0.741705821961581 )*( Car_Model_Mahindra TUV 300 )  + ( -2.3483166522453303e-09 )*( Car_Model_Mahindra Thar 4X4 )  + ( -3.002437047798636 )*( Car_Model_Mahindra Thar CRDe )  + ( -4.258099892676192 )*( Car_Model_Mahindra Thar DI )  + ( 1.135412576554235 )*( Car_Model_Mahindra Verito 1.5 )  + ( 3.06670809549907 )*( Car_Model_Mahindra Verito Vibe )  + ( 5.042590555055887 )*( Car_Model_Mahindra XUV300 W8 )  + ( 3.9506892410094014 )*( Car_Model_Mahindra XUV500 AT )  + ( 3.483012389924656 )*( Car_Model_Mahindra XUV500 W10 )  + ( 0.5190186031857644 )*( Car_Model_Mahindra XUV500 W4 )  + ( -0.6396811292460989 )*( Car_Model_Mahindra XUV500 W6 )  + ( 1.2133600790775745e-09 )*( Car_Model_Mahindra XUV500 W7 )  + ( 0.548184141012743 )*( Car_Model_Mahindra XUV500 W8 )  + ( 3.1238326144560493 )*( Car_Model_Mahindra XUV500 W9 )  + ( -4.356158405354476 )*( Car_Model_Mahindra Xylo D2 )  + ( -4.053932317190394 )*( Car_Model_Mahindra Xylo D4 )  + ( -2.2424227991716985 )*( Car_Model_Mahindra Xylo E2 )  + ( -1.5927132025064885 )*( Car_Model_Mahindra Xylo E4 )  + ( -1.867346089383209 )*( Car_Model_Mahindra Xylo E8 )  + ( -1.8466839427289752 )*( Car_Model_Mahindra Xylo E9 )  + ( -2.1421156196261744 )*( Car_Model_Mahindra Xylo H4 )  + ( 1.1824177192920615e-09 )*( Car_Model_Mahindra Xylo H9 )  + ( 9.921876085263442 )*( Car_Model_Maruti 1000 AC )  + ( 4.131374748760631 )*( Car_Model_Maruti 800 AC )  + ( 9.515331069270431 )*( Car_Model_Maruti 800 DX )  + ( 6.891656509599988 )*( Car_Model_Maruti 800 Std )  + ( -1.5395296881564824 )*( Car_Model_Maruti A-Star AT )  + ( 1.1983862690738065 )*( Car_Model_Maruti A-Star Lxi )  + ( 1.9069003717526298 )*( Car_Model_Maruti A-Star Vxi )  + ( 4.569943612726937 )*( Car_Model_Maruti A-Star Zxi )  + ( -1.7339321107091472 )*( Car_Model_Maruti Alto 800 )  + ( 1.2368583844592842 )*( Car_Model_Maruti Alto Green )  + ( -1.8031505308266287 )*( Car_Model_Maruti Alto K10 )  + ( 4.211421229426978 )*( Car_Model_Maruti Alto LX )  + ( 4.595125941462749 )*( Car_Model_Maruti Alto LXI )  + ( 3.098620088548195 )*( Car_Model_Maruti Alto LXi )  + ( 5.124237597108805 )*( Car_Model_Maruti Alto Std )  + ( 2.2712405289837534e-09 )*( Car_Model_Maruti Alto VXi )  + ( -3.94317467566907e-10 )*( Car_Model_Maruti Alto Vxi )  + ( 9.6374996871873e-10 )*( Car_Model_Maruti Alto XCITE )  + ( -1.244346274025036 )*( Car_Model_Maruti Baleno Alpha )  + ( -2.2893159514361106 )*( Car_Model_Maruti Baleno Delta )  + ( 2.0493086151612956 )*( Car_Model_Maruti Baleno LXI )  + ( -0.857783307119143 )*( Car_Model_Maruti Baleno RS )  + ( -2.4037934580521627 )*( Car_Model_Maruti Baleno Sigma )  + ( 3.791115622120809 )*( Car_Model_Maruti Baleno Vxi )  + ( -1.9068921178000429 )*( Car_Model_Maruti Baleno Zeta )  + ( -0.5262952700746893 )*( Car_Model_Maruti Celerio CNG )  + ( -2.8897143767872597 )*( Car_Model_Maruti Celerio LDi )  + ( -2.3833427729726404 )*( Car_Model_Maruti Celerio LXI )  + ( -2.045993423432533 )*( Car_Model_Maruti Celerio VXI )  + ( -2.3388846415173248e-09 )*( Car_Model_Maruti Celerio X )  + ( -1.3743692556306089 )*( Car_Model_Maruti Celerio ZDi )  + ( -1.8436713812590972 )*( Car_Model_Maruti Celerio ZXI )  + ( -1.3645274397784848 )*( Car_Model_Maruti Ciaz 1.3 )  + ( -1.3616932046288346 )*( Car_Model_Maruti Ciaz 1.4 )  + ( -0.3899400299675812 )*( Car_Model_Maruti Ciaz AT )  + ( -1.080496087132 )*( Car_Model_Maruti Ciaz Alpha )  + ( -0.4854927578798532 )*( Car_Model_Maruti Ciaz RS )  + ( -1.1536975555464377 )*( Car_Model_Maruti Ciaz VDI )  + ( -1.282678346235596 )*( Car_Model_Maruti Ciaz VDi )  + ( -1.1523391558682292 )*( Car_Model_Maruti Ciaz VXi )  + ( -0.5058527301910886 )*( Car_Model_Maruti Ciaz ZDi )  + ( -0.7732952586377614 )*( Car_Model_Maruti Ciaz ZXi )  + ( -0.6063026554989641 )*( Car_Model_Maruti Ciaz Zeta )  + ( -3.203699281806661 )*( Car_Model_Maruti Dzire AMT )  + ( -2.54773405715078 )*( Car_Model_Maruti Dzire LDI )  + ( -0.9602961819183937 )*( Car_Model_Maruti Dzire New )  + ( -0.7713083900050908 )*( Car_Model_Maruti Dzire VDI )  + ( 1.1873655569381125 )*( Car_Model_Maruti Dzire VXI )  + ( -1.5135665475537003 )*( Car_Model_Maruti Dzire ZDI )  + ( -2.5997176728419142 )*( Car_Model_Maruti Eeco 5 )  + ( -3.8443283884007617 )*( Car_Model_Maruti Eeco 7 )  + ( -2.191573884590432 )*( Car_Model_Maruti Eeco CNG )  + ( -4.798151881329146 )*( Car_Model_Maruti Eeco Smiles )  + ( 1.1833956037321514e-10 )*( Car_Model_Maruti Ertiga LXI )  + ( -2.3983306652202274 )*( Car_Model_Maruti Ertiga Paseo )  + ( -2.191768831634 )*( Car_Model_Maruti Ertiga SHVS )  + ( -1.2179375770525431 )*( Car_Model_Maruti Ertiga VDI )  + ( -2.0153119125666263 )*( Car_Model_Maruti Ertiga VXI )  + ( -0.541524230230473 )*( Car_Model_Maruti Ertiga ZDI )  + ( -1.6157950652511854 )*( Car_Model_Maruti Ertiga ZXI )  + ( 3.671316981437138 )*( Car_Model_Maruti Esteem LX )  + ( 3.5086484792197257 )*( Car_Model_Maruti Esteem Vxi )  + ( 1.1610069653731032 )*( Car_Model_Maruti Estilo LXI )  + ( -0.866434770868288 )*( Car_Model_Maruti Grand Vitara )  + ( -3.7139680546431775 )*( Car_Model_Maruti Ignis 1.2 )  + ( -2.810103006611625 )*( Car_Model_Maruti Ignis 1.3 )  + ( -1.7872989040274558 )*( Car_Model_Maruti Omni 5 )  + ( -1.9894754086888358 )*( Car_Model_Maruti Omni 8 )  + ( -4.438684613436882 )*( Car_Model_Maruti Omni E )  + ( 0.23882028204326322 )*( Car_Model_Maruti Omni MPI )  + ( -1.3168773489964718 )*( Car_Model_Maruti Ritz AT )  + ( -0.9495368825145949 )*( Car_Model_Maruti Ritz LDi )  + ( -0.7153169918961177 )*( Car_Model_Maruti Ritz LXI )  + ( -0.04294053527832159 )*( Car_Model_Maruti Ritz LXi )  + ( -2.6030967286055797 )*( Car_Model_Maruti Ritz VDI )  + ( -0.6384657363672437 )*( Car_Model_Maruti Ritz VDi )  + ( -1.1989869344177282 )*( Car_Model_Maruti Ritz VXI )  + ( -0.4048755060129931 )*( Car_Model_Maruti Ritz VXi )  + ( -2.321830803976412 )*( Car_Model_Maruti Ritz ZDi )  + ( -2.1283953710590646 )*( Car_Model_Maruti Ritz ZXI )  + ( 1.8089068710507819 )*( Car_Model_Maruti Ritz ZXi )  + ( -0.7151422802275897 )*( Car_Model_Maruti S Cross )  + ( -0.26676455258897935 )*( Car_Model_Maruti S-Cross Alpha )  + ( -0.7104312835990434 )*( Car_Model_Maruti S-Cross Delta )  + ( 7.324629791582993e-11 )*( Car_Model_Maruti S-Cross Zeta )  + ( -3.06210855992735 )*( Car_Model_Maruti SX4 Green )  + ( -0.42184462712008985 )*( Car_Model_Maruti SX4 S )  + ( 0.9675556928661297 )*( Car_Model_Maruti SX4 VDI )  + ( 0.65430684116298 )*( Car_Model_Maruti SX4 Vxi )  + ( -0.5944577990143829 )*( Car_Model_Maruti SX4 ZDI )  + ( -0.8257746724059629 )*( Car_Model_Maruti SX4 ZXI )  + ( 0.9611386695910714 )*( Car_Model_Maruti SX4 Zxi )  + ( 0.05771731591008478 )*( Car_Model_Maruti Swift 1.3 )  + ( -2.4889145300679116 )*( Car_Model_Maruti Swift AMT )  + ( -1.5252577255680477 )*( Car_Model_Maruti Swift DDiS )  + ( -0.45747138756893113 )*( Car_Model_Maruti Swift Dzire )  + ( -1.3533339884539 )*( Car_Model_Maruti Swift LDI )  + ( -0.05997471871102092 )*( Car_Model_Maruti Swift LXI )  + ( 0.6735809741882799 )*( Car_Model_Maruti Swift LXi )  + ( 0.12807186765013343 )*( Car_Model_Maruti Swift Ldi )  + ( 1.0060296201579348 )*( Car_Model_Maruti Swift Lxi )  + ( -0.6171570909657265 )*( Car_Model_Maruti Swift RS )  + ( -1.1760771099436833 )*( Car_Model_Maruti Swift VDI )  + ( 0.8611637137704429 )*( Car_Model_Maruti Swift VDi )  + ( -1.7301447196053916 )*( Car_Model_Maruti Swift VVT )  + ( -0.8548202295823475 )*( Car_Model_Maruti Swift VXI )  + ( -1.6278983513359393 )*( Car_Model_Maruti Swift VXi )  + ( 1.8333074111495964 )*( Car_Model_Maruti Swift Vdi )  + ( -0.15835828440624744 )*( Car_Model_Maruti Swift ZDI )  + ( -0.4016106967857252 )*( Car_Model_Maruti Swift ZDi )  + ( -0.6317498149153521 )*( Car_Model_Maruti Swift ZXI )  + ( 3.563488775867781 )*( Car_Model_Maruti Versa DX2 )  + ( -0.8075831991674722 )*( Car_Model_Maruti Vitara Brezza )  + ( 0.2910556682673733 )*( Car_Model_Maruti Wagon R )  + ( 2.3936830867855368 )*( Car_Model_Maruti Zen Estilo )  + ( 7.482677912858012 )*( Car_Model_Maruti Zen LX )  + ( 6.697686854832119 )*( Car_Model_Maruti Zen LXI )  + ( 4.592109857760713 )*( Car_Model_Maruti Zen LXi )  + ( 7.832725579248745e-10 )*( Car_Model_Maruti Zen VX )  + ( 4.862227986574918 )*( Car_Model_Maruti Zen VXI )  + ( -1.4076206866775465e-10 )*( Car_Model_Maruti Zen VXi )  + ( -14.108458852339412 )*( Car_Model_Mercedes-Benz A Class )  + ( -11.565801061361423 )*( Car_Model_Mercedes-Benz B Class )  + ( 7.91216394384194 )*( Car_Model_Mercedes-Benz C-Class Progressive )  + ( -0.6876372405007629 )*( Car_Model_Mercedes-Benz CLA 200 )  + ( -19.0775557388545 )*( Car_Model_Mercedes-Benz CLA 45 )  + ( 4.270114503342965 )*( Car_Model_Mercedes-Benz CLS-Class 2006-2010 )  + ( -6.540721611271315 )*( Car_Model_Mercedes-Benz E-Class 200 )  + ( -11.539737996172477 )*( Car_Model_Mercedes-Benz E-Class 2009-2013 )  + ( 0.38407993256649703 )*( Car_Model_Mercedes-Benz E-Class 2015-2017 )  + ( 6.678728681208668e-10 )*( Car_Model_Mercedes-Benz E-Class 220 )  + ( -15.90148006842765 )*( Car_Model_Mercedes-Benz E-Class 230 )  + ( -15.318615386075301 )*( Car_Model_Mercedes-Benz E-Class 250 )  + ( -15.715683550328974 )*( Car_Model_Mercedes-Benz E-Class 280 )  + ( 4.986484268058847 )*( Car_Model_Mercedes-Benz E-Class E )  + ( -13.276419611228823 )*( Car_Model_Mercedes-Benz E-Class E240 )  + ( -9.255062953713509 )*( Car_Model_Mercedes-Benz E-Class E250 )  + ( -19.051777841653838 )*( Car_Model_Mercedes-Benz E-Class E270 )  + ( -3.0197578125194675 )*( Car_Model_Mercedes-Benz E-Class E350 )  + ( 17.250985913910714 )*( Car_Model_Mercedes-Benz E-Class E400 )  + ( 14.543664426520987 )*( Car_Model_Mercedes-Benz E-Class Facelift )  + ( 6.65741047884321 )*( Car_Model_Mercedes-Benz GL-Class 2007 )  + ( -2.7668998006047394 )*( Car_Model_Mercedes-Benz GL-Class 350 )  + ( -6.694432609944506 )*( Car_Model_Mercedes-Benz GLA Class )  + ( 12.977427830908683 )*( Car_Model_Mercedes-Benz GLC 220 )  + ( 8.198382682421524 )*( Car_Model_Mercedes-Benz GLC 220d )  + ( 34.6972482364725 )*( Car_Model_Mercedes-Benz GLC 43 )  + ( 9.700002361974912 )*( Car_Model_Mercedes-Benz GLE 250d )  + ( 27.660114946896574 )*( Car_Model_Mercedes-Benz GLE 350d )  + ( 20.775686638167222 )*( Car_Model_Mercedes-Benz GLS 350d )  + ( 0.2201589061856244 )*( Car_Model_Mercedes-Benz M-Class ML )  + ( -6.777771484889743 )*( Car_Model_Mercedes-Benz New C-Class )  + ( -10.282507868321403 )*( Car_Model_Mercedes-Benz R-Class R350 )  + ( -7.832953491603485 )*( Car_Model_Mercedes-Benz S Class )  + ( -1.2229328660851024e-10 )*( Car_Model_Mercedes-Benz S-Class 280 )  + ( -18.671937582166812 )*( Car_Model_Mercedes-Benz S-Class 320 )  + ( -4.528684094395885e-10 )*( Car_Model_Mercedes-Benz S-Class S )  + ( -1.9037699221378068 )*( Car_Model_Mercedes-Benz SL-Class SL )  + ( -29.4041137720387 )*( Car_Model_Mercedes-Benz SLC 43 )  + ( 52.5773891675687 )*( Car_Model_Mercedes-Benz SLK-Class 55 )  + ( 24.219788273422555 )*( Car_Model_Mercedes-Benz SLK-Class SLK )  + ( -3.8862457036295974 )*( Car_Model_Mini Clubman Cooper )  + ( -4.480752133265615 )*( Car_Model_Mini Cooper 3 )  + ( 7.114069889631228 )*( Car_Model_Mini Cooper 5 )  + ( 11.13177173386228 )*( Car_Model_Mini Cooper Convertible )  + ( -5.0476793097531605 )*( Car_Model_Mini Cooper Countryman )  + ( 2.3554076529455896 )*( Car_Model_Mini Cooper S )  + ( -2.2618303973135605 )*( Car_Model_Mini Countryman Cooper )  + ( -2.6379132846738824 )*( Car_Model_Mitsubishi Cedia Sports )  + ( 3.584185136080688 )*( Car_Model_Mitsubishi Lancer 1.5 )  + ( 1.6271604345074018 )*( Car_Model_Mitsubishi Lancer GLXD )  + ( 4.2649439535580314e-11 )*( Car_Model_Mitsubishi Montero 3.2 )  + ( -1.6754127184734664 )*( Car_Model_Mitsubishi Outlander 2.4 )  + ( -2.9014806387645558 )*( Car_Model_Mitsubishi Pajero 2.8 )  + ( 4.409628218127182e-10 )*( Car_Model_Mitsubishi Pajero 4X4 )  + ( -2.8158656426950133 )*( Car_Model_Mitsubishi Pajero Sport )  + ( 2.709050761495746e-10 )*( Car_Model_Nissan 370Z AT )  + ( -2.1170581842800074 )*( Car_Model_Nissan Evalia 2013 )  + ( -1.87301737990207 )*( Car_Model_Nissan Micra Active )  + ( -0.4085725211796107 )*( Car_Model_Nissan Micra Diesel )  + ( 1.6834555264619715 )*( Car_Model_Nissan Micra XE )  + ( -1.6889174515692922 )*( Car_Model_Nissan Micra XL )  + ( -0.8579664682036835 )*( Car_Model_Nissan Micra XV )  + ( -0.46721272434963645 )*( Car_Model_Nissan Sunny 2011-2014 )  + ( -0.09594056567717191 )*( Car_Model_Nissan Sunny Diesel )  + ( 1.5168538092247275e-09 )*( Car_Model_Nissan Sunny XE )  + ( 0.6481937984668027 )*( Car_Model_Nissan Sunny XL )  + ( -0.22658193440680593 )*( Car_Model_Nissan Sunny XV )  + ( -0.24732507489787892 )*( Car_Model_Nissan Teana 230jM )  + ( -3.2639064784234506e-09 )*( Car_Model_Nissan Teana XL )  + ( -1.929931600637352 )*( Car_Model_Nissan Teana XV )  + ( -1.1975220814974818e-09 )*( Car_Model_Nissan Terrano XE )  + ( -0.671716422112077 )*( Car_Model_Nissan Terrano XL )  + ( -0.34440219244596243 )*( Car_Model_Nissan Terrano XV )  + ( 2.633734535938451 )*( Car_Model_Nissan X-Trail SLX )  + ( 0.6079725942674566 )*( Car_Model_OpelCorsa 1.4Gsi )  + ( -3.5344045468121976e-09 )*( Car_Model_Porsche Boxster S )  + ( -9.81576322265926 )*( Car_Model_Porsche Cayenne 2009-2014 )  + ( 6.965166221561958e-10 )*( Car_Model_Porsche Cayenne Base )  + ( -10.692095078715731 )*( Car_Model_Porsche Cayenne Diesel )  + ( 20.263225469816092 )*( Car_Model_Porsche Cayenne S )  + ( -11.324768724286091 )*( Car_Model_Porsche Cayenne Turbo )  + ( 2.532565744764411 )*( Car_Model_Porsche Cayman 2009-2012 )  + ( 3.686686511628068e-10 )*( Car_Model_Porsche Panamera 2010 )  + ( 22.572712680480727 )*( Car_Model_Porsche Panamera Diesel )  + ( 3.490321087117756 )*( Car_Model_Renault Captur 1.5 )  + ( 1.3637673836873185 )*( Car_Model_Renault Duster 110PS )  + ( 0.5652817231882616 )*( Car_Model_Renault Duster 85PS )  + ( 0.7379029187021282 )*( Car_Model_Renault Duster Adventure )  + ( -0.8806206394103264 )*( Car_Model_Renault Duster Petrol )  + ( 1.2891962033271744 )*( Car_Model_Renault Duster RXZ )  + ( 1.464563137432151e-09 )*( Car_Model_Renault Fluence 1.5 )  + ( 1.701714596492876e-09 )*( Car_Model_Renault Fluence 2.0 )  + ( 0.3904280276961986 )*( Car_Model_Renault Fluence Diesel )  + ( -1.708041655822103 )*( Car_Model_Renault KWID 1.0 )  + ( -2.792269669870057 )*( Car_Model_Renault KWID AMT )  + ( -1.9005959891736128 )*( Car_Model_Renault KWID Climber )  + ( -1.2523796919149488 )*( Car_Model_Renault KWID RXL )  + ( -1.1657690557284925 )*( Car_Model_Renault KWID RXT )  + ( 1.8535816446311146 )*( Car_Model_Renault Koleos 2.0 )  + ( -2.222960197280253 )*( Car_Model_Renault Koleos 4X2 )  + ( -1.8686740821748906 )*( Car_Model_Renault Lodgy 110PS )  + ( -0.8034351725265498 )*( Car_Model_Renault Pulse Petrol )  + ( -0.4123833631039936 )*( Car_Model_Renault Pulse RxL )  + ( 0.20723280397098687 )*( Car_Model_Renault Pulse RxZ )  + ( -0.4860486699398001 )*( Car_Model_Renault Scala Diesel )  + ( -1.320937304439901 )*( Car_Model_Renault Scala RxL )  + ( -0.8016048042243666 )*( Car_Model_Skoda Fabia 1.2 )  + ( -0.7170108972799389 )*( Car_Model_Skoda Fabia 1.2L )  + ( 0.45378952890282526 )*( Car_Model_Skoda Fabia 1.4 )  + ( -3.5773925756844447 )*( Car_Model_Skoda Fabia 1.6 )  + ( -1.1607619857042566 )*( Car_Model_Skoda Laura 1.8 )  + ( -0.29629446418906924 )*( Car_Model_Skoda Laura 1.9 )  + ( -1.230465458387492 )*( Car_Model_Skoda Laura Ambiente )  + ( -2.20248287993252 )*( Car_Model_Skoda Laura Ambition )  + ( -1.5761278477105547 )*( Car_Model_Skoda Laura Classic )  + ( -1.585518725654154 )*( Car_Model_Skoda Laura Elegance )  + ( -1.6861593445277794 )*( Car_Model_Skoda Laura L )  + ( 0.06062631887486033 )*( Car_Model_Skoda Laura RS )  + ( -1.3669148618168876 )*( Car_Model_Skoda Octavia 1.9 )  + ( -1.4384052570840413 )*( Car_Model_Skoda Octavia 2.0 )  + ( -0.34792943969402185 )*( Car_Model_Skoda Octavia Ambiente )  + ( 4.586455271823926 )*( Car_Model_Skoda Octavia Ambition )  + ( 2.3199189651001406 )*( Car_Model_Skoda Octavia Classic )  + ( 4.54022775302195 )*( Car_Model_Skoda Octavia Elegance )  + ( 0.4199541565504339 )*( Car_Model_Skoda Octavia L )  + ( 0.2717918161444811 )*( Car_Model_Skoda Octavia RS )  + ( -1.1410366518147448 )*( Car_Model_Skoda Octavia Rider )  + ( 4.524313785864264 )*( Car_Model_Skoda Octavia Style )  + ( -1.276337260837405 )*( Car_Model_Skoda Rapid 1.5 )  + ( -1.490461310980941 )*( Car_Model_Skoda Rapid 1.6 )  + ( -6.275753250406524e-10 )*( Car_Model_Skoda Rapid 2013-2016 )  + ( 6.987259924297329e-11 )*( Car_Model_Skoda Rapid Leisure )  + ( -1.842819957654956 )*( Car_Model_Skoda Rapid Ultima )  + ( 0.5813017441766493 )*( Car_Model_Skoda Superb 1.8 )  + ( -2.909180459861177 )*( Car_Model_Skoda Superb 2.5 )  + ( -1.514596966664797 )*( Car_Model_Skoda Superb 2.8 )  + ( 4.654164898259982 )*( Car_Model_Skoda Superb 2009-2014 )  + ( -7.721813877988384 )*( Car_Model_Skoda Superb 3.6 )  + ( 2.0320456428635225e-10 )*( Car_Model_Skoda Superb Ambition )  + ( 0.8599967172469061 )*( Car_Model_Skoda Superb Elegance )  + ( 2.589419315143288 )*( Car_Model_Skoda Superb L&K )  + ( 0.7299045384057715 )*( Car_Model_Skoda Superb Petrol )  + ( 3.6634608929246806 )*( Car_Model_Skoda Superb Style )  + ( -0.42943932181110694 )*( Car_Model_Skoda Yeti Ambition )  + ( 0.33944288725123983 )*( Car_Model_Skoda Yeti Elegance )  + ( 0.25771636981450463 )*( Car_Model_Smart Fortwo CDI )  + ( -2.444227195391259 )*( Car_Model_Tata Bolt Quadrajet )  + ( 2.6337243497209784e-10 )*( Car_Model_Tata Bolt Revotron )  + ( 0.874706607445221 )*( Car_Model_Tata Hexa XT )  + ( 0.6965769124398935 )*( Car_Model_Tata Hexa XTA )  + ( 2.136032931024537 )*( Car_Model_Tata Indica DLS )  + ( 7.721645545188949e-11 )*( Car_Model_Tata Indica GLS )  + ( 3.977340988714072 )*( Car_Model_Tata Indica LEI )  + ( -0.748425109115006 )*( Car_Model_Tata Indica V2 )  + ( 1.8784933119749452 )*( Car_Model_Tata Indica Vista )  + ( -0.9290419303314716 )*( Car_Model_Tata Indigo CS )  + ( -3.543341617709976 )*( Car_Model_Tata Indigo GLE )  + ( 3.324944665317847 )*( Car_Model_Tata Indigo LS )  + ( 1.3230739264418077 )*( Car_Model_Tata Indigo LX )  + ( 1.1576948961407223 )*( Car_Model_Tata Indigo XL )  + ( -0.443147895414745 )*( Car_Model_Tata Indigo eCS )  + ( 2.0224840683181298 )*( Car_Model_Tata Manza Aqua )  + ( 0.8525420956472187 )*( Car_Model_Tata Manza Aura )  + ( -0.6484928969332877 )*( Car_Model_Tata Manza Club )  + ( 1.4731212858798994 )*( Car_Model_Tata Manza ELAN )  + ( 1.2435183728689203 )*( Car_Model_Tata Nano CX )  + ( 2.8363630764606205 )*( Car_Model_Tata Nano Cx )  + ( 0.9005671931942102 )*( Car_Model_Tata Nano LX )  + ( 3.553405575082648 )*( Car_Model_Tata Nano Lx )  + ( 1.8976464843944996e-10 )*( Car_Model_Tata Nano STD )  + ( -0.09918811331511887 )*( Car_Model_Tata Nano Twist )  + ( 1.5064759965818009 )*( Car_Model_Tata Nano XT )  + ( -0.0014043864340946843 )*( Car_Model_Tata Nano XTA )  + ( -2.037831298606083 )*( Car_Model_Tata New Safari )  + ( 7.79714071086346e-11 )*( Car_Model_Tata Nexon 1.2 )  + ( 0.8706339659389274 )*( Car_Model_Tata Nexon 1.5 )  + ( -3.702838761197363 )*( Car_Model_Tata Safari DICOR )  + ( -2.0161874449021964 )*( Car_Model_Tata Safari Storme )  + ( -4.2542853839083525 )*( Car_Model_Tata Sumo DX )  + ( 2.2130421251428025 )*( Car_Model_Tata Sumo Delux )  + ( -6.083418887146702 )*( Car_Model_Tata Sumo EX )  + ( -6.435385557779227e-11 )*( Car_Model_Tata Tiago 1.05 )  + ( -1.2278033464744025 )*( Car_Model_Tata Tiago 1.2 )  + ( -2.6144471990082367 )*( Car_Model_Tata Tiago AMT )  + ( -1.6394781533129146 )*( Car_Model_Tata Tiago Wizz )  + ( 0.014327863785712956 )*( Car_Model_Tata Tigor 1.05 )  + ( -1.2100105987174825 )*( Car_Model_Tata Tigor 1.2 )  + ( -0.8312521981471609 )*( Car_Model_Tata Tigor XE )  + ( -2.899737562189113e-11 )*( Car_Model_Tata Venture EX )  + ( -3.609786818458268 )*( Car_Model_Tata Xenon XT )  + ( -1.8910577186616468 )*( Car_Model_Tata Zest Quadrajet )  + ( 0.19488952233704915 )*( Car_Model_Tata Zest Revotron )  + ( 5.118965120570897 )*( Car_Model_Toyota Camry 2.5 )  + ( 3.292477401828364e-11 )*( Car_Model_Toyota Camry A/T )  + ( 10.576070106854209 )*( Car_Model_Toyota Camry Hybrid )  + ( 1.1940048949554694e-10 )*( Car_Model_Toyota Camry MT )  + ( -3.2700317104760908 )*( Car_Model_Toyota Camry W2 )  + ( -4.269302651535636 )*( Car_Model_Toyota Camry W4 )  + ( -0.026406510181604115 )*( Car_Model_Toyota Corolla 1.8 )  + ( 0.6528199598759694 )*( Car_Model_Toyota Corolla Altis )  + ( 8.329878083010661e-11 )*( Car_Model_Toyota Corolla DX )  + ( 3.491755883573047 )*( Car_Model_Toyota Corolla Executive )  + ( 0.10745836295866551 )*( Car_Model_Toyota Corolla H2 )  + ( 1.0230437916369244 )*( Car_Model_Toyota Corolla H4 )  + ( 1.165606345827681 )*( Car_Model_Toyota Corolla H5 )  + ( -2.991665413224293 )*( Car_Model_Toyota Etios 1.4 )  + ( -2.219191802271373 )*( Car_Model_Toyota Etios Cross )  + ( -0.7411987405623341 )*( Car_Model_Toyota Etios G )  + ( -0.9184763782482881 )*( Car_Model_Toyota Etios GD )  + ( -1.2836428543159932 )*( Car_Model_Toyota Etios Liva )  + ( -2.6751934001367772e-11 )*( Car_Model_Toyota Etios Petrol )  + ( -1.886134843191383 )*( Car_Model_Toyota Etios V )  + ( -1.0194310073126729 )*( Car_Model_Toyota Etios VD )  + ( -2.0108235102268273 )*( Car_Model_Toyota Etios VX )  + ( 3.086420008457935e-11 )*( Car_Model_Toyota Etios VXD )  + ( 5.960229442281437 )*( Car_Model_Toyota Fortuner 2.8 )  + ( -2.024440380662331 )*( Car_Model_Toyota Fortuner 3.0 )  + ( 0.37971029532093503 )*( Car_Model_Toyota Fortuner 4x2 )  + ( 3.127949091157508 )*( Car_Model_Toyota Fortuner 4x4 )  + ( 2.524344821139287 )*( Car_Model_Toyota Fortuner TRD )  + ( -1.3203331227067332 )*( Car_Model_Toyota Innova 2.0 )  + ( -2.656029373540512 )*( Car_Model_Toyota Innova 2.5 )  + ( -0.7311599734033853 )*( Car_Model_Toyota Innova Crysta )  + ( -9.067941702952734 )*( Car_Model_Toyota Land Cruiser )  + ( -1.122500988940022 )*( Car_Model_Toyota Platinum Etios )  + ( -8.881784197001252e-16 )*( Car_Model_Toyota Prius 2009-2016 )  + ( -0.4701558654732677 )*( Car_Model_Toyota Qualis FS )  + ( 3.246109774751073e-16 )*( Car_Model_Toyota Qualis Fleet )  + ( -1.0359874110404603 )*( Car_Model_Toyota Qualis RS )  + ( -2.363086081069164 )*( Car_Model_Volkswagen Ameo 1.2 )  + ( -4.079671033227187 )*( Car_Model_Volkswagen Ameo 1.5 )  + ( 0.0 )*( Car_Model_Volkswagen Beetle 2.0 )  + ( 0.5736221984067734 )*( Car_Model_Volkswagen CrossPolo 1.2 )  + ( -2.2305237054344196 )*( Car_Model_Volkswagen CrossPolo 1.5 )  + ( 0.6756919515907723 )*( Car_Model_Volkswagen Jetta 2007-2011 )  + ( 1.9858608924021979 )*( Car_Model_Volkswagen Jetta 2012-2014 )  + ( 1.4742550854543033 )*( Car_Model_Volkswagen Jetta 2013-2015 )  + ( -0.14723683418707717 )*( Car_Model_Volkswagen Passat 1.8 )  + ( 0.8955200545678759 )*( Car_Model_Volkswagen Passat 2.0 )  + ( 0.6182882988350833 )*( Car_Model_Volkswagen Passat Diesel )  + ( 0.4894843762313829 )*( Car_Model_Volkswagen Passat Highline )  + ( -1.0259608725179958 )*( Car_Model_Volkswagen Polo 1.0 )  + ( -1.178875742663517 )*( Car_Model_Volkswagen Polo 1.2 )  + ( -2.514489495940827 )*( Car_Model_Volkswagen Polo 1.5 )  + ( 0.0 )*( Car_Model_Volkswagen Polo ALLSTAR )  + ( -0.0039973462963058815 )*( Car_Model_Volkswagen Polo Diesel )  + ( -0.5150235194354482 )*( Car_Model_Volkswagen Polo GT )  + ( -2.528031086019375 )*( Car_Model_Volkswagen Polo GTI )  + ( 0.0 )*( Car_Model_Volkswagen Polo IPL )  + ( 0.03749543512032494 )*( Car_Model_Volkswagen Polo Petrol )  + ( 10.940115972234786 )*( Car_Model_Volkswagen Tiguan 2.0 )  + ( -0.315743776495345 )*( Car_Model_Volkswagen Vento 1.2 )  + ( -1.0934159970835164 )*( Car_Model_Volkswagen Vento 1.5 )  + ( -1.2964964258930316 )*( Car_Model_Volkswagen Vento 1.6 )  + ( -2.4328618430173563 )*( Car_Model_Volkswagen Vento 2013-2015 )  + ( -0.6074714468387112 )*( Car_Model_Volkswagen Vento Diesel )  + ( -1.4096239445013006 )*( Car_Model_Volkswagen Vento IPL )  + ( 0.0 )*( Car_Model_Volkswagen Vento Konekt )  + ( 0.0 )*( Car_Model_Volkswagen Vento Magnific )  + ( -0.7651591143093752 )*( Car_Model_Volkswagen Vento Petrol )  + ( 0.3450757072523935 )*( Car_Model_Volkswagen Vento Sport )  + ( 1.0007947786053575 )*( Car_Model_Volkswagen Vento TSI )  + ( 0.0 )*( Car_Model_Volvo S60 D3 )  + ( 1.7081575965521496 )*( Car_Model_Volvo S60 D4 )  + ( 1.0748544817728631 )*( Car_Model_Volvo S60 D5 )  + ( -6.769302944002041 )*( Car_Model_Volvo S80 2006-2013 )  + ( -4.928445857056805 )*( Car_Model_Volvo S80 D5 )  + ( 2.0774003776281296 )*( Car_Model_Volvo V40 Cross )  + ( 4.308275345555871 )*( Car_Model_Volvo V40 D3 )  + ( -2.685279205065372 )*( Car_Model_Volvo XC60 D4 )  + ( 4.078033462357802 )*( Car_Model_Volvo XC60 D5 )  + ( 0.0 )*( Car_Model_Volvo XC90 2007-2015 )

Since the Price column is skewed, we will modify the Price column to log of Price, rebuild the model, & compare the score to the original model.

In [82]:
# creating a copy of the dataframe

df8 = df7.copy(deep=True)
In [83]:
# checking the new dataframe

df8.head()
Out[83]:
Kilometers_Driven Mileage Engine Seats New_Price Price Car_Age Location_Ahmedabad Location_Bangalore Location_Chennai Location_Coimbatore Location_Delhi Location_Hyderabad Location_Jaipur Location_Kochi Location_Kolkata Location_Mumbai Location_Pune Fuel_Type_CNG Fuel_Type_Diesel Fuel_Type_Electric Fuel_Type_LPG Fuel_Type_Petrol Transmission_Automatic Transmission_Manual Owner_Type_First Owner_Type_Fourth & Above Owner_Type_Second Owner_Type_Third Car_Brand_Ambassador Car_Brand_Audi Car_Brand_BMW Car_Brand_Bentley Car_Brand_Chevrolet Car_Brand_Datsun Car_Brand_Fiat Car_Brand_Force Car_Brand_Ford Car_Brand_Hindustan Car_Brand_Honda Car_Brand_Hyundai Car_Brand_ISUZU Car_Brand_Isuzu Car_Brand_Jaguar Car_Brand_Jeep Car_Brand_Lamborghini Car_Brand_Land Car_Brand_Mahindra Car_Brand_Maruti Car_Brand_Mercedes-Benz Car_Brand_Mini Car_Brand_Mitsubishi Car_Brand_Nissan Car_Brand_OpelCorsa Car_Brand_Porsche Car_Brand_Renault Car_Brand_Skoda Car_Brand_Smart Car_Brand_Tata Car_Brand_Toyota Car_Brand_Volkswagen Car_Brand_Volvo Car_Model_Ambassador Classic Nova Car_Model_Audi A3 35 Car_Model_Audi A4 1.8 Car_Model_Audi A4 2.0 Car_Model_Audi A4 3.0 Car_Model_Audi A4 3.2 Car_Model_Audi A4 30 Car_Model_Audi A4 35 Car_Model_Audi A4 New Car_Model_Audi A6 2.0 Car_Model_Audi A6 2.7 Car_Model_Audi A6 2.8 Car_Model_Audi A6 2011-2015 Car_Model_Audi A6 3.0 Car_Model_Audi A6 35 Car_Model_Audi A7 2011-2015 Car_Model_Audi A8 L Car_Model_Audi Q3 2.0 Car_Model_Audi Q3 2012-2015 Car_Model_Audi Q3 30 Car_Model_Audi Q3 35 Car_Model_Audi Q5 2.0 Car_Model_Audi Q5 2008-2012 Car_Model_Audi Q5 3.0 Car_Model_Audi Q5 30 Car_Model_Audi Q7 3.0 Car_Model_Audi Q7 35 Car_Model_Audi Q7 4.2 Car_Model_Audi Q7 45 Car_Model_Audi RS5 Coupe Car_Model_Audi TT 2.0 Car_Model_Audi TT 40 Car_Model_BMW 1 Series Car_Model_BMW 3 Series Car_Model_BMW 5 Series Car_Model_BMW 6 Series Car_Model_BMW 7 Series Car_Model_BMW X1 M Car_Model_BMW X1 sDrive Car_Model_BMW X1 sDrive20d Car_Model_BMW X1 xDrive Car_Model_BMW X3 2.5si Car_Model_BMW X3 xDrive Car_Model_BMW X3 xDrive20d Car_Model_BMW X3 xDrive30d Car_Model_BMW X5 2014-2019 Car_Model_BMW X5 3.0d Car_Model_BMW X5 X5 Car_Model_BMW X5 xDrive Car_Model_BMW X6 xDrive Car_Model_BMW X6 xDrive30d Car_Model_BMW Z4 2009-2013 Car_Model_Bentley Continental Flying Car_Model_Bentley Flying Spur Car_Model_Chevrolet Aveo 1.4 Car_Model_Chevrolet Aveo 1.6 Car_Model_Chevrolet Aveo U-VA Car_Model_Chevrolet Beat Diesel Car_Model_Chevrolet Beat LS Car_Model_Chevrolet Beat LT Car_Model_Chevrolet Beat Option Car_Model_Chevrolet Captiva LT Car_Model_Chevrolet Captiva LTZ Car_Model_Chevrolet Cruze LTZ Car_Model_Chevrolet Enjoy 1.3 Car_Model_Chevrolet Enjoy 1.4 Car_Model_Chevrolet Enjoy Petrol Car_Model_Chevrolet Enjoy TCDi Car_Model_Chevrolet Optra 1.6 Car_Model_Chevrolet Optra Magnum Car_Model_Chevrolet Sail 1.2 Car_Model_Chevrolet Sail Hatchback Car_Model_Chevrolet Sail LT Car_Model_Chevrolet Spark 1.0 Car_Model_Chevrolet Tavera LS Car_Model_Chevrolet Tavera LT Car_Model_Datsun GO NXT Car_Model_Datsun GO Plus Car_Model_Datsun GO T Car_Model_Datsun Redi GO Car_Model_Datsun redi-GO S Car_Model_Datsun redi-GO T Car_Model_Fiat Abarth 595 Car_Model_Fiat Avventura FIRE Car_Model_Fiat Avventura MULTIJET Car_Model_Fiat Avventura Urban Car_Model_Fiat Grande Punto Car_Model_Fiat Linea 1.3 Car_Model_Fiat Linea Classic Car_Model_Fiat Linea Dynamic Car_Model_Fiat Linea Emotion Car_Model_Fiat Linea T Car_Model_Fiat Linea T-Jet Car_Model_Fiat Petra 1.2 Car_Model_Fiat Punto 1.2 Car_Model_Fiat Punto 1.3 Car_Model_Fiat Punto 1.4 Car_Model_Fiat Punto EVO Car_Model_Fiat Siena 1.2 Car_Model_Force One LX Car_Model_Ford Aspire Ambiente Car_Model_Ford Aspire Titanium Car_Model_Ford Classic 1.4 Car_Model_Ford EcoSport 1.0 Car_Model_Ford EcoSport 1.5 Car_Model_Ford Ecosport 1.0 Car_Model_Ford Ecosport 1.5 Car_Model_Ford Ecosport Signature Car_Model_Ford Endeavour 2.2 Car_Model_Ford Endeavour 2.5L Car_Model_Ford Endeavour 3.0L Car_Model_Ford Endeavour 3.2 Car_Model_Ford Endeavour 4x2 Car_Model_Ford Endeavour Hurricane Car_Model_Ford Endeavour Titanium Car_Model_Ford Endeavour XLT Car_Model_Ford Fiesta 1.4 Car_Model_Ford Fiesta 1.5 Car_Model_Ford Fiesta 1.6 Car_Model_Ford Fiesta Classic Car_Model_Ford Fiesta Diesel Car_Model_Ford Fiesta EXi Car_Model_Ford Fiesta Titanium Car_Model_Ford Figo 1.2P Car_Model_Ford Figo 1.5D Car_Model_Ford Figo 2015-2019 Car_Model_Ford Figo Aspire Car_Model_Ford Figo Diesel Car_Model_Ford Figo Petrol Car_Model_Ford Figo Titanium Car_Model_Ford Freestyle Titanium Car_Model_Ford Fusion Plus Car_Model_Ford Ikon 1.3 Car_Model_Ford Ikon 1.4 Car_Model_Ford Ikon 1.6 Car_Model_Ford Mustang V8 Car_Model_Hindustan Motors Contessa Car_Model_Honda Accord 2.4 Car_Model_Honda Accord 2001-2003 Car_Model_Honda Accord V6 Car_Model_Honda Accord VTi-L Car_Model_Honda Amaze E Car_Model_Honda Amaze EX Car_Model_Honda Amaze S Car_Model_Honda Amaze SX Car_Model_Honda Amaze V Car_Model_Honda Amaze VX Car_Model_Honda BR-V i-DTEC Car_Model_Honda BR-V i-VTEC Car_Model_Honda BRV i-DTEC Car_Model_Honda BRV i-VTEC Car_Model_Honda Brio 1.2 Car_Model_Honda Brio E Car_Model_Honda Brio EX Car_Model_Honda Brio S Car_Model_Honda Brio V Car_Model_Honda Brio VX Car_Model_Honda CR-V 2.0 Car_Model_Honda CR-V 2.0L Car_Model_Honda CR-V 2.4 Car_Model_Honda CR-V 2.4L Car_Model_Honda CR-V AT Car_Model_Honda CR-V Diesel Car_Model_Honda CR-V Petrol Car_Model_Honda CR-V RVi Car_Model_Honda CR-V Sport Car_Model_Honda City 1.3 Car_Model_Honda City 1.5 Car_Model_Honda City Corporate Car_Model_Honda City V Car_Model_Honda City ZX Car_Model_Honda City i Car_Model_Honda City i-DTEC Car_Model_Honda City i-VTEC Car_Model_Honda Civic 2006-2010 Car_Model_Honda Civic 2010-2013 Car_Model_Honda Jazz 1.2 Car_Model_Honda Jazz 1.5 Car_Model_Honda Jazz 2020 Car_Model_Honda Jazz Active Car_Model_Honda Jazz Exclusive Car_Model_Honda Jazz Mode Car_Model_Honda Jazz S Car_Model_Honda Jazz Select Car_Model_Honda Jazz V Car_Model_Honda Jazz VX 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1 41000 19.67 1582.0 5.0 16.06 12.50 7 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
2 46000 18.20 1199.0 5.0 8.61 4.50 11 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
3 87000 20.77 1248.0 7.0 11.27 6.00 10 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
4 40670 15.20 1968.0 5.0 53.14 17.74 9 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
In [84]:
# creating a new column for the log of the Price column

df8['PriceLog'] = np.log(df8['Price'])
In [85]:
# dropping the original price column from df8

df8.drop(['Price'], axis=1, inplace=True)

Now we will rebuild the model using the new dataframe wth the PriceLog column.

In [86]:
# dropping PriceLog from X to prepare for modelling

X2 = df8.drop("PriceLog", axis=1)
y2 = df8["PriceLog"]
In [87]:
# splitting the dataset into training set (70%) and test set (30%)

X2_train, X2_test, y2_train, y2_test = train_test_split( X2, y2, test_size=0.30, random_state=1)
In [88]:
# fitting the model to the training set

regression_model2 = LinearRegression()
regression_model2.fit(X2_train, y2_train)
Out[88]:
LinearRegression()
In [89]:
# calculating the R-squared value of the training set

print(regression_model2.score(X2_train, y2_train),)
0.8322530502381619
In [90]:
# calculating the RMSE value of thee training set

print(np.sqrt(mean_squared_error(y2_train, regression_model2.predict(X2_train))))
0.32236078870501333
In [91]:
# calculating the R-squared value of the test set

print(regression_model2.score(X2_test, y2_test),)
0.7482294559497418
In [92]:
# calculating the RMSE value of the test set

print(np.sqrt(mean_squared_error(y2_test, regression_model2.predict(X2_test))))
0.4112469644486728

We see that the R-squared values have improved significantly for both the training and test set in the model using the PriceLog column. The distance between the R-squared values of the training and test set has decreased too (~16 in the original model vs ~9 in the model using PriceLog).

Let's now look at the coefficients and intercept, and get the linear regression equation of the new model with PriceLog.

In [97]:
# taking a look at the coefficients and intercepts 

equationinfo2 = pd.DataFrame(
    np.append(regression_model2.coef_, regression_model2.intercept_),
    index=X2_train.columns.tolist() + ["Intercept"],
    columns=["Coefficients"],
)

equationinfo2
Out[97]:
Coefficients
Kilometers_Driven -8.746626e-07
Mileage 2.372166e-03
Engine 1.307825e-04
Seats -5.057720e-03
New_Price -1.445279e-03
... ...
Car_Model_Volvo V40 D3 2.572566e-01
Car_Model_Volvo XC60 D4 -2.047931e-01
Car_Model_Volvo XC60 D5 3.523821e-01
Car_Model_Volvo XC90 2007-2015 0.000000e+00
Intercept 2.728658e+00

789 rows × 1 columns

In [95]:
# exporting the linear regression equation 

Equation = "PriceLog = " + str(regression_model2.intercept_)
print(Equation, end=" ")

for i in range(len(X2_train.columns)):
    if i != len(X2_train.columns) - 1:
        print(
            "+ (",
            regression_model2.coef_[i],
            ")*(",
            X2_train.columns[i],
            ")",
            end="  ",
        )
    else:
        print("+ (", regression_model2.coef_[i], ")*(", X2_train.columns[i], ")")
PriceLog = 2.728658111367454 + ( -8.746625875166784e-07 )*( Kilometers_Driven )  + ( 0.0023721661368460555 )*( Mileage )  + ( 0.00013078249827094823 )*( Engine )  + ( -0.005057720001762703 )*( Seats )  + ( -0.0014452791810755494 )*( New_Price )  + ( -0.08877105951718677 )*( Car_Age )  + ( -0.0061298469711264375 )*( Location_Ahmedabad )  + ( 0.10253933082473624 )*( Location_Bangalore )  + ( 0.054121423002741524 )*( Location_Chennai )  + ( 0.08453324745818235 )*( Location_Coimbatore )  + ( -0.08050051851080384 )*( Location_Delhi )  + ( 0.1007126470987004 )*( Location_Hyderabad )  + ( 0.003181217191724095 )*( Location_Jaipur )  + ( -0.02958194871716721 )*( Location_Kochi )  + ( -0.1805438421315043 )*( Location_Kolkata )  + ( -0.03502815609039317 )*( Location_Mumbai )  + ( -0.013303556220963464 )*( Location_Pune )  + ( -0.008501835061563323 )*( Fuel_Type_CNG )  + ( 0.025598708579790103 )*( Fuel_Type_Diesel )  + ( -2.665352660802256e-10 )*( Fuel_Type_Electric )  + ( -0.04190542364211243 )*( Fuel_Type_LPG )  + ( 0.02480854978615507 )*( Fuel_Type_Petrol )  + ( 0.036432001198884394 )*( Transmission_Automatic )  + ( -0.036432000810544984 )*( Transmission_Manual )  + ( 0.025053623020723594 )*( Owner_Type_First )  + ( 0.15415604311425352 )*( Owner_Type_Fourth & Above )  + ( -0.01112482464028025 )*( Owner_Type_Second )  + ( -0.1680848399608718 )*( Owner_Type_Third )  + ( -0.36530614178035453 )*( Car_Brand_Ambassador )  + ( 0.7198513893668984 )*( Car_Brand_Audi )  + ( 0.7866958448019796 )*( Car_Brand_BMW )  + ( 0.7529545879492949 )*( Car_Brand_Bentley )  + ( -0.6865052177207207 )*( Car_Brand_Chevrolet )  + ( -1.0087423434156924 )*( Car_Brand_Datsun )  + ( -0.6280479949880322 )*( Car_Brand_Fiat )  + ( 0.0380799306815293 )*( Car_Brand_Force )  + ( -0.4759766158724176 )*( Car_Brand_Ford )  + ( 0.5743249355501038 )*( Car_Brand_Hindustan )  + ( -0.48043732762779734 )*( Car_Brand_Honda )  + ( -0.5630398205529434 )*( Car_Brand_Hyundai )  + ( -0.11042820690859799 )*( Car_Brand_ISUZU )  + ( -2.485722738754248e-11 )*( Car_Brand_Isuzu )  + ( 0.6670720908622209 )*( Car_Brand_Jaguar )  + ( 0.19268787645616473 )*( Car_Brand_Jeep )  + ( 1.279509255376961 )*( Car_Brand_Lamborghini )  + ( 0.7276235043705586 )*( Car_Brand_Land )  + ( -0.4210493548256429 )*( Car_Brand_Mahindra )  + ( -0.6708977787085311 )*( Car_Brand_Maruti )  + ( 0.7141039866121138 )*( Car_Brand_Mercedes-Benz )  + ( 0.47707584924909935 )*( Car_Brand_Mini )  + ( -0.18084370603508443 )*( Car_Brand_Mitsubishi )  + ( -0.5217988874139101 )*( Car_Brand_Nissan )  + ( 0.15119066471561435 )*( Car_Brand_OpelCorsa )  + ( 1.2009216211728597 )*( Car_Brand_Porsche )  + ( -0.587372838130078 )*( Car_Brand_Renault )  + ( -0.3200916260588807 )*( Car_Brand_Skoda )  + ( -0.206405596639174 )*( Car_Brand_Smart )  + ( -0.9065071655728553 )*( Car_Brand_Tata )  + ( -0.12416239143606977 )*( Car_Brand_Toyota )  + ( -0.4071621506752631 )*( Car_Brand_Volkswagen )  + ( 0.3826836266634654 )*( Car_Brand_Volvo )  + ( -0.3653061418241263 )*( Car_Model_Ambassador Classic Nova )  + ( -0.7821980382492619 )*( Car_Model_Audi A3 35 )  + ( -0.33068446224539483 )*( Car_Model_Audi A4 1.8 )  + ( -0.18744125308949997 )*( Car_Model_Audi A4 2.0 )  + ( -0.4905152865629715 )*( Car_Model_Audi A4 3.0 )  + ( 1.4990078622822978e-11 )*( Car_Model_Audi A4 3.2 )  + ( -0.060437978664326125 )*( Car_Model_Audi A4 30 )  + ( -0.20237754373806402 )*( Car_Model_Audi A4 35 )  + ( -0.1580928214272021 )*( Car_Model_Audi A4 New )  + ( -0.09073492086787718 )*( Car_Model_Audi A6 2.0 )  + ( -0.24344830521027083 )*( Car_Model_Audi A6 2.7 )  + ( -5.3888726814221855e-11 )*( Car_Model_Audi A6 2.8 )  + ( -0.4166740154203166 )*( Car_Model_Audi A6 2011-2015 )  + ( 0.01237914548776059 )*( Car_Model_Audi A6 3.0 )  + ( 0.10776638860212735 )*( Car_Model_Audi A6 35 )  + ( 0.5939568641242885 )*( Car_Model_Audi A7 2011-2015 )  + ( -0.1351895727709343 )*( Car_Model_Audi A8 L )  + ( 0.07635682688188314 )*( Car_Model_Audi Q3 2.0 )  + ( -0.36019008784759265 )*( Car_Model_Audi Q3 2012-2015 )  + ( -0.8334579102503297 )*( Car_Model_Audi Q3 30 )  + ( -0.33610380740393375 )*( Car_Model_Audi Q3 35 )  + ( 0.06991894392661274 )*( Car_Model_Audi Q5 2.0 )  + ( -0.24415169798658914 )*( Car_Model_Audi Q5 2008-2012 )  + ( 0.24583485776309025 )*( Car_Model_Audi Q5 3.0 )  + ( 0.55345643779533 )*( Car_Model_Audi Q5 30 )  + ( 0.35782165184518866 )*( Car_Model_Audi Q7 3.0 )  + ( 0.6465708807454567 )*( Car_Model_Audi Q7 35 )  + ( 0.37734870423790606 )*( Car_Model_Audi Q7 4.2 )  + ( 0.8755922527659513 )*( Car_Model_Audi Q7 45 )  + ( 0.7760572766261548 )*( Car_Model_Audi RS5 Coupe )  + ( 7.528966339265253e-11 )*( Car_Model_Audi TT 2.0 )  + ( 0.8984888605448427 )*( Car_Model_Audi TT 40 )  + ( -0.31711515181342786 )*( Car_Model_BMW 1 Series )  + ( -0.3232587984886003 )*( Car_Model_BMW 3 Series )  + ( -0.12859038509999823 )*( Car_Model_BMW 5 Series )  + ( 0.11456203805788341 )*( Car_Model_BMW 6 Series )  + ( 0.15490272207226388 )*( Car_Model_BMW 7 Series )  + ( 0.10813671561160833 )*( Car_Model_BMW X1 M )  + ( -0.13513622065154446 )*( Car_Model_BMW X1 sDrive )  + ( -0.500701257969554 )*( Car_Model_BMW X1 sDrive20d )  + ( -0.4755693176714666 )*( Car_Model_BMW X1 xDrive )  + ( -0.9677888942352985 )*( Car_Model_BMW X3 2.5si )  + ( 0.35241337450240773 )*( Car_Model_BMW X3 xDrive )  + ( 0.023232636592046014 )*( Car_Model_BMW X3 xDrive20d )  + ( 0.3467487214867898 )*( Car_Model_BMW X3 xDrive30d )  + ( 0.580594296203974 )*( Car_Model_BMW X5 2014-2019 )  + ( 0.15399629718939944 )*( Car_Model_BMW X5 3.0d )  + ( 0.7023395757980817 )*( Car_Model_BMW X5 X5 )  + ( 0.09141040889794663 )*( Car_Model_BMW X5 xDrive )  + ( 0.8653362421139076 )*( Car_Model_BMW X6 xDrive )  + ( -0.40423918149884874 )*( Car_Model_BMW X6 xDrive30d )  + ( 0.5454220239761667 )*( Car_Model_BMW Z4 2009-2013 )  + ( 1.628615199424958 )*( Car_Model_Bentley Continental Flying )  + ( -0.8756606113465726 )*( Car_Model_Bentley Flying Spur )  + ( -0.28009231668958884 )*( Car_Model_Chevrolet Aveo 1.4 )  + ( 0.059281010336928376 )*( Car_Model_Chevrolet Aveo 1.6 )  + ( -0.31480574115534393 )*( Car_Model_Chevrolet Aveo U-VA )  + ( -0.36521969502031826 )*( Car_Model_Chevrolet Beat Diesel )  + ( -0.1848255613305036 )*( Car_Model_Chevrolet Beat LS )  + ( -0.22221119372645026 )*( Car_Model_Chevrolet Beat LT )  + ( -0.49684001378003173 )*( Car_Model_Chevrolet Beat Option )  + ( 0.42228034065152487 )*( Car_Model_Chevrolet Captiva LT )  + ( -2.889133376982045e-12 )*( Car_Model_Chevrolet Captiva LTZ )  + ( 0.24903843610495008 )*( Car_Model_Chevrolet Cruze LTZ )  + ( 0.14743499090581413 )*( Car_Model_Chevrolet Enjoy 1.3 )  + ( 0.06574919259881595 )*( Car_Model_Chevrolet Enjoy 1.4 )  + ( -0.24940985737762908 )*( Car_Model_Chevrolet Enjoy Petrol )  + ( -0.06081064659989144 )*( Car_Model_Chevrolet Enjoy TCDi )  + ( -0.10724480748193493 )*( Car_Model_Chevrolet Optra 1.6 )  + ( -0.15564640326631649 )*( Car_Model_Chevrolet Optra Magnum )  + ( -0.04912790726337617 )*( Car_Model_Chevrolet Sail 1.2 )  + ( 0.01599960276137602 )*( Car_Model_Chevrolet Sail Hatchback )  + ( -0.39206370427588993 )*( Car_Model_Chevrolet Sail LT )  + ( -0.05380565230052438 )*( Car_Model_Chevrolet Spark 1.0 )  + ( 0.9542593742834502 )*( Car_Model_Chevrolet Tavera LS )  + ( 0.33155533516994296 )*( Car_Model_Chevrolet Tavera LT )  + ( -0.19319363587869864 )*( Car_Model_Datsun GO NXT )  + ( -0.08784179794082242 )*( Car_Model_Datsun GO Plus )  + ( 0.1578703873786228 )*( Car_Model_Datsun GO T )  + ( -0.37504254986996594 )*( Car_Model_Datsun Redi GO )  + ( -0.3469420231498291 )*( Car_Model_Datsun redi-GO S )  + ( -0.16359272392179608 )*( Car_Model_Datsun redi-GO T )  + ( -0.1517366683202043 )*( Car_Model_Fiat Abarth 595 )  + ( 3.1099012254287572e-12 )*( Car_Model_Fiat Avventura FIRE )  + ( 0.1144164320340599 )*( Car_Model_Fiat Avventura MULTIJET )  + ( -0.11038877679108439 )*( Car_Model_Fiat Avventura Urban )  + ( 0.07777471400621148 )*( Car_Model_Fiat Grande Punto )  + ( -0.47142488651627673 )*( Car_Model_Fiat Linea 1.3 )  + ( 0.387436821839993 )*( Car_Model_Fiat Linea Classic )  + ( 0.5812195517999408 )*( Car_Model_Fiat Linea Dynamic )  + ( -0.11284054631456643 )*( Car_Model_Fiat Linea Emotion )  + ( -1.0122458427019865e-11 )*( Car_Model_Fiat Linea T )  + ( 0.07464987320395185 )*( Car_Model_Fiat Linea T-Jet )  + ( -0.7738056211073422 )*( Car_Model_Fiat Petra 1.2 )  + ( 3.5328739933504494e-11 )*( Car_Model_Fiat Punto 1.2 )  + ( -0.33421742609892424 )*( Car_Model_Fiat Punto 1.3 )  + ( 0.05901502957474434 )*( Car_Model_Fiat Punto 1.4 )  + ( 0.0318535075696549 )*( Car_Model_Fiat Punto EVO )  + ( -2.255218234381573e-11 )*( Car_Model_Fiat Siena 1.2 )  + ( 0.03807993057667169 )*( Car_Model_Force One LX )  + ( -0.20635974488229863 )*( Car_Model_Ford Aspire Ambiente )  + ( 0.21503865403689093 )*( Car_Model_Ford Aspire Titanium )  + ( 2.626066031297114e-11 )*( Car_Model_Ford Classic 1.4 )  + ( 0.10802194407057626 )*( Car_Model_Ford EcoSport 1.0 )  + ( 0.10914365371985814 )*( Car_Model_Ford EcoSport 1.5 )  + ( 0.04475878593587665 )*( Car_Model_Ford Ecosport 1.0 )  + ( 0.11055500574136436 )*( Car_Model_Ford Ecosport 1.5 )  + ( 0.234774322129794 )*( Car_Model_Ford Ecosport Signature )  + ( 0.8544272654314006 )*( Car_Model_Ford Endeavour 2.2 )  + ( 0.41242177427517845 )*( Car_Model_Ford Endeavour 2.5L )  + ( 0.30424599468743935 )*( Car_Model_Ford Endeavour 3.0L )  + ( 1.1417670621749603 )*( Car_Model_Ford Endeavour 3.2 )  + ( -0.1400770076381832 )*( Car_Model_Ford Endeavour 4x2 )  + ( 0.20616176856241486 )*( Car_Model_Ford Endeavour Hurricane )  + ( 1.0057835296168767 )*( Car_Model_Ford Endeavour Titanium )  + ( 8.580007104103116e-11 )*( Car_Model_Ford Endeavour XLT )  + ( -0.02423494431284815 )*( Car_Model_Ford Fiesta 1.4 )  + ( -0.03144525662338521 )*( Car_Model_Ford Fiesta 1.5 )  + ( -0.5445031210606456 )*( Car_Model_Ford Fiesta 1.6 )  + ( -0.146090791711956 )*( Car_Model_Ford Fiesta Classic )  + ( 0.20422343680110416 )*( Car_Model_Ford Fiesta Diesel )  + ( -0.3515023005074599 )*( Car_Model_Ford Fiesta EXi )  + ( 2.081779193474631e-12 )*( Car_Model_Ford Fiesta Titanium )  + ( -0.14702534279759136 )*( Car_Model_Ford Figo 1.2P )  + ( -0.037980169594170145 )*( Car_Model_Ford Figo 1.5D )  + ( -0.29983326718094566 )*( Car_Model_Ford Figo 2015-2019 )  + ( -0.2235868669971194 )*( Car_Model_Ford Figo Aspire )  + ( -0.3685557931129352 )*( Car_Model_Ford Figo Diesel )  + ( -0.31801329294937747 )*( Car_Model_Ford Figo Petrol )  + ( -0.7057209493040859 )*( Car_Model_Ford Figo Titanium )  + ( -0.06876736466703837 )*( Car_Model_Ford Freestyle Titanium )  + ( -0.1072045782424278 )*( Car_Model_Ford Fusion Plus )  + ( -0.5003010276379917 )*( Car_Model_Ford Ikon 1.3 )  + ( -0.46461566980959346 )*( Car_Model_Ford Ikon 1.4 )  + ( -0.7414823241709086 )*( Car_Model_Ford Ikon 1.6 )  + ( 3.998620878853387e-11 )*( Car_Model_Ford Mustang V8 )  + ( 0.5743249355040653 )*( Car_Model_Hindustan Motors Contessa )  + ( 0.17812711116434682 )*( Car_Model_Honda Accord 2.4 )  + ( 0.34323237270920404 )*( Car_Model_Honda Accord 2001-2003 )  + ( -0.22099098153204513 )*( Car_Model_Honda Accord V6 )  + ( -0.09439836723990204 )*( Car_Model_Honda Accord VTi-L )  + ( -0.1675638919819732 )*( Car_Model_Honda Amaze E )  + ( -0.21366727590662676 )*( Car_Model_Honda Amaze EX )  + ( -0.16110379463669483 )*( Car_Model_Honda Amaze S )  + ( -0.16591155305852986 )*( Car_Model_Honda Amaze SX )  + ( -0.09242438491378514 )*( Car_Model_Honda Amaze V )  + ( -0.11568825745248118 )*( Car_Model_Honda Amaze VX )  + ( 8.44188607906915e-12 )*( Car_Model_Honda BR-V i-DTEC )  + ( 0.13323482556095176 )*( Car_Model_Honda BR-V i-VTEC )  + ( -0.29271702736474486 )*( Car_Model_Honda BRV i-DTEC )  + ( 0.11725178440691329 )*( Car_Model_Honda BRV i-VTEC )  + ( -0.10101928101833188 )*( Car_Model_Honda Brio 1.2 )  + ( -0.1626948810474158 )*( Car_Model_Honda Brio E )  + ( -0.5627624647557788 )*( Car_Model_Honda Brio EX )  + ( -0.27574569670768134 )*( Car_Model_Honda Brio S )  + ( -0.22456265726520053 )*( Car_Model_Honda Brio V )  + ( -0.23271463525189043 )*( Car_Model_Honda Brio VX )  + ( 0.6875401826804687 )*( Car_Model_Honda CR-V 2.0 )  + ( 0.5707039639511888 )*( Car_Model_Honda CR-V 2.0L )  + ( 0.4075250377991754 )*( Car_Model_Honda CR-V 2.4 )  + ( 0.6280460864699081 )*( Car_Model_Honda CR-V 2.4L )  + ( 1.9853119148649512e-11 )*( Car_Model_Honda CR-V AT )  + ( 0.056419292583602225 )*( Car_Model_Honda CR-V Diesel )  + ( 0.39746095543896126 )*( Car_Model_Honda CR-V Petrol )  + ( 0.6319277900664141 )*( Car_Model_Honda CR-V RVi )  + ( 0.7536848077424109 )*( Car_Model_Honda CR-V Sport )  + ( -0.18930996465418454 )*( Car_Model_Honda City 1.3 )  + ( 0.03267719135792049 )*( Car_Model_Honda City 1.5 )  + ( -0.03729903969444056 )*( Car_Model_Honda City Corporate )  + ( 0.10972213475805717 )*( Car_Model_Honda City V )  + ( -0.08183067968178603 )*( Car_Model_Honda City ZX )  + ( 0.14646293386354214 )*( Car_Model_Honda City i )  + ( 0.2744741451114095 )*( Car_Model_Honda City i-DTEC )  + ( 0.14009127635580523 )*( Car_Model_Honda City i-VTEC )  + ( 0.017864112171662012 )*( Car_Model_Honda Civic 2006-2010 )  + ( -0.14715912100858572 )*( Car_Model_Honda Civic 2010-2013 )  + ( -0.09127253989990265 )*( Car_Model_Honda Jazz 1.2 )  + ( -0.08191751045317626 )*( Car_Model_Honda Jazz 1.5 )  + ( -0.34197859672795805 )*( Car_Model_Honda Jazz 2020 )  + ( -0.44837700064279623 )*( Car_Model_Honda Jazz Active )  + ( 0.007467320413383227 )*( Car_Model_Honda Jazz Exclusive )  + ( -0.41431712591002773 )*( Car_Model_Honda Jazz Mode )  + ( -0.3315646490761603 )*( Car_Model_Honda Jazz S )  + ( -0.33926551873245236 )*( Car_Model_Honda Jazz Select )  + ( -0.01399808833332485 )*( Car_Model_Honda Jazz V )  + ( -0.1559743751294137 )*( Car_Model_Honda Jazz VX )  + ( -0.006790767952652447 )*( Car_Model_Honda Mobilio E )  + ( -0.03803472018324093 )*( Car_Model_Honda Mobilio RS )  + ( -0.058828248921638626 )*( Car_Model_Honda Mobilio S )  + ( 0.038766761174769994 )*( Car_Model_Honda Mobilio V )  + ( -1.370806801404001e-10 )*( Car_Model_Honda WR-V Edge )  + ( -0.43719383060958744 )*( Car_Model_Honda WRV i-DTEC )  + ( 0.14595951382010003 )*( Car_Model_Honda WRV i-VTEC )  + ( -0.007819196939378686 )*( Car_Model_Hyundai Accent CRDi )  + ( -0.021094489754532184 )*( Car_Model_Hyundai Accent Executive )  + ( -0.1543215615602928 )*( Car_Model_Hyundai Accent GLE )  + ( -0.47946514579893157 )*( Car_Model_Hyundai Accent GLS )  + ( 0.620091419290892 )*( Car_Model_Hyundai Accent GLX )  + ( 0.2884743920796712 )*( Car_Model_Hyundai Creta 1.4 )  + ( 0.45336328205492293 )*( Car_Model_Hyundai Creta 1.6 )  + ( -0.04166493821137541 )*( Car_Model_Hyundai EON 1.0 )  + ( -0.39564348575448216 )*( Car_Model_Hyundai EON D )  + ( -0.378806106040658 )*( Car_Model_Hyundai EON Era )  + ( -4.1372225090263726e-11 )*( Car_Model_Hyundai EON LPG )  + ( -0.447094916683925 )*( Car_Model_Hyundai EON Magna )  + ( -0.4561379138806164 )*( Car_Model_Hyundai EON Sportz )  + ( 0.45102843168797674 )*( Car_Model_Hyundai Elantra 1.6 )  + ( 0.4185340883340664 )*( Car_Model_Hyundai Elantra 2.0 )  + ( 0.5066143917483991 )*( Car_Model_Hyundai Elantra CRDi )  + ( 0.7385897992083783 )*( Car_Model_Hyundai Elantra GT )  + ( -0.03367362828945744 )*( Car_Model_Hyundai Elantra SX )  + ( 0.05114722458431497 )*( Car_Model_Hyundai Elite i20 )  + ( -0.7465750140357924 )*( Car_Model_Hyundai Getz 1.3 )  + ( -0.515594276895821 )*( Car_Model_Hyundai Getz 1.5 )  + ( 0.039761604170021024 )*( Car_Model_Hyundai Getz GLE )  + ( -0.6754301903931218 )*( Car_Model_Hyundai Getz GLS )  + ( 0.42510794537276336 )*( Car_Model_Hyundai Getz GVS )  + ( -0.14307018480653502 )*( Car_Model_Hyundai Grand i10 )  + ( 0.8027037821043487 )*( Car_Model_Hyundai Santa Fe )  + ( -0.13301863872712008 )*( Car_Model_Hyundai Santro AT )  + ( -0.568381500083245 )*( Car_Model_Hyundai Santro D )  + ( 1.4588996677389332e-11 )*( Car_Model_Hyundai Santro DX )  + ( -0.24250659679918374 )*( Car_Model_Hyundai Santro GLS )  + ( -0.26357917092254624 )*( Car_Model_Hyundai Santro GS )  + ( -0.1245513736849561 )*( Car_Model_Hyundai Santro LP )  + ( -0.7443319403285087 )*( Car_Model_Hyundai Santro LS )  + ( -0.30203803991853756 )*( Car_Model_Hyundai Santro Xing )  + ( 0.43607595009765654 )*( Car_Model_Hyundai Sonata 2.4 )  + ( 0.7475383855403244 )*( Car_Model_Hyundai Sonata Embera )  + ( -0.31504039407388146 )*( Car_Model_Hyundai Sonata GOLD )  + ( 0.5396574643740866 )*( Car_Model_Hyundai Sonata Transform )  + ( 0.345808692319356 )*( Car_Model_Hyundai Tucson 2.0 )  + ( 0.29955629530539624 )*( Car_Model_Hyundai Tucson CRDi )  + ( 0.1504072962169293 )*( Car_Model_Hyundai Verna 1.4 )  + ( 0.19569337528527328 )*( Car_Model_Hyundai Verna 1.6 )  + ( 0.07132891108694062 )*( Car_Model_Hyundai Verna CRDi )  + ( 0.3127119766147326 )*( Car_Model_Hyundai Verna SX )  + ( -0.06833574389258401 )*( Car_Model_Hyundai Verna Transform )  + ( 0.1928516594700468 )*( Car_Model_Hyundai Verna VTVT )  + ( -0.21006915867160406 )*( Car_Model_Hyundai Verna XXi )  + ( -0.3195623226369872 )*( Car_Model_Hyundai Verna Xi )  + ( -0.10523276092006598 )*( Car_Model_Hyundai Xcent 1.1 )  + ( -0.16729970761241666 )*( Car_Model_Hyundai Xcent 1.2 )  + ( -0.16481409542035932 )*( Car_Model_Hyundai i10 Asta )  + ( -0.2558601446653229 )*( Car_Model_Hyundai i10 Era )  + ( -0.11524385460458886 )*( Car_Model_Hyundai i10 Magna )  + ( -0.2740291288918358 )*( Car_Model_Hyundai i10 Magna(O) )  + ( -0.18547112803355215 )*( Car_Model_Hyundai i10 Sportz )  + ( 0.020480184868568244 )*( Car_Model_Hyundai i20 1.2 )  + ( 0.010792936352713 )*( Car_Model_Hyundai i20 1.4 )  + ( 0.0890730563991019 )*( Car_Model_Hyundai i20 2015-2017 )  + ( 0.09241953986791693 )*( Car_Model_Hyundai i20 Active )  + ( 0.06679482877098047 )*( Car_Model_Hyundai i20 Asta )  + ( 0.06741827559252214 )*( Car_Model_Hyundai i20 Diesel )  + ( -0.11369239847139492 )*( Car_Model_Hyundai i20 Era )  + ( 0.024233633228163187 )*( Car_Model_Hyundai i20 Magna )  + ( -0.012577944818039713 )*( Car_Model_Hyundai i20 Sportz )  + ( 0.1607284490997654 )*( Car_Model_Hyundai i20 new )  + ( -0.11042820683484611 )*( Car_Model_ISUZU D-MAX V-Cross )  + ( -9.653322585734259e-11 )*( Car_Model_Isuzu MU 7 )  + ( -3.3678865252184664e-11 )*( Car_Model_Isuzu MUX 4WD )  + ( -6.718037237618546e-11 )*( Car_Model_Jaguar F Type )  + ( -1.555414986566791 )*( Car_Model_Jaguar XE 2.0L )  + ( -1.6545233893203122 )*( Car_Model_Jaguar XE Portfolio )  + ( 0.6529252426146023 )*( Car_Model_Jaguar XF 2.0 )  + ( 0.1826756281672303 )*( Car_Model_Jaguar XF 2.2 )  + ( 0.06355980420553262 )*( Car_Model_Jaguar XF 3.0 )  + ( 0.767331808063815 )*( Car_Model_Jaguar XF Aero )  + ( 0.00150800469802366 )*( Car_Model_Jaguar XF Diesel )  + ( 1.1657167090415173 )*( Car_Model_Jaguar XJ 2.0L )  + ( 1.043293270040092 )*( Car_Model_Jaguar XJ 3.0L )  + ( -2.1558144158717596e-11 )*( Car_Model_Jaguar XJ 5.0 )  + ( 0.19377515336242795 )*( Car_Model_Jeep Compass 1.4 )  + ( -0.0010872769978605493 )*( Car_Model_Jeep Compass 2.0 )  + ( 1.2795092555119056 )*( Car_Model_Lamborghini Gallardo Coupe )  + ( 0.12690482797035244 )*( Car_Model_Land Rover Discovery )  + ( -0.036329495454409985 )*( Car_Model_Land Rover Freelander )  + ( 0.6370481719277057 )*( Car_Model_Land Rover Range )  + ( -0.12463568557875564 )*( Car_Model_Mahindra Bolero DI )  + ( -0.4879122867553673 )*( Car_Model_Mahindra Bolero Power )  + ( -0.19937466422931338 )*( Car_Model_Mahindra Bolero SLE )  + ( 3.103253765068814e-12 )*( Car_Model_Mahindra Bolero SLX )  + ( -0.028432828856224314 )*( Car_Model_Mahindra Bolero VLX )  + ( -0.17243354871804353 )*( Car_Model_Mahindra Bolero ZLX )  + ( -0.047096808230272964 )*( Car_Model_Mahindra Bolero mHAWK )  + ( 2.506295171400552e-11 )*( Car_Model_Mahindra E Verito )  + ( -0.22290249944475737 )*( Car_Model_Mahindra Jeep MM )  + ( -0.31552408475523225 )*( Car_Model_Mahindra KUV 100 )  + ( -0.5852914289337036 )*( Car_Model_Mahindra Logan Diesel )  + ( -0.90042621871663 )*( Car_Model_Mahindra Logan Petrol )  + ( -0.18199526329589513 )*( Car_Model_Mahindra NuvoSport N6 )  + ( -2.587097203132771e-12 )*( Car_Model_Mahindra NuvoSport N8 )  + ( -0.4965661881129102 )*( Car_Model_Mahindra Quanto C2 )  + ( -0.2944815985866718 )*( Car_Model_Mahindra Quanto C4 )  + ( 1.0176796905181362e-11 )*( Car_Model_Mahindra Quanto C6 )  + ( -0.2944848073405579 )*( Car_Model_Mahindra Quanto C8 )  + ( 0.1563157747107521 )*( Car_Model_Mahindra Renault Logan )  + ( 0.3880862212893568 )*( Car_Model_Mahindra Scorpio 1.99 )  + ( 0.042237931132080256 )*( Car_Model_Mahindra Scorpio 2.6 )  + ( 0.20662764461745176 )*( Car_Model_Mahindra Scorpio 2009-2014 )  + ( 0.08718497330218011 )*( Car_Model_Mahindra Scorpio DX )  + ( -0.08768230742038807 )*( Car_Model_Mahindra Scorpio LX )  + ( 0.28800981315557633 )*( Car_Model_Mahindra Scorpio S10 )  + ( -6.280787001600174e-11 )*( Car_Model_Mahindra Scorpio S2 )  + ( 0.023910241135444668 )*( Car_Model_Mahindra Scorpio S4 )  + ( 0.26364492164060793 )*( Car_Model_Mahindra Scorpio S6 )  + ( 0.43093559374358176 )*( Car_Model_Mahindra Scorpio S8 )  + ( 0.08185794766613279 )*( Car_Model_Mahindra Scorpio SLE )  + ( 0.2547373668416967 )*( Car_Model_Mahindra Scorpio SLX )  + ( 0.4278659822217259 )*( Car_Model_Mahindra Scorpio VLS )  + ( 0.25653308438907046 )*( Car_Model_Mahindra Scorpio VLX )  + ( 0.44445871172399226 )*( Car_Model_Mahindra Ssangyong Rexton )  + ( -0.02228012314334661 )*( Car_Model_Mahindra TUV 300 )  + ( -1.3208290017274749e-10 )*( Car_Model_Mahindra Thar 4X4 )  + ( -0.1168225682240556 )*( Car_Model_Mahindra Thar CRDe )  + ( -0.13318303348584734 )*( Car_Model_Mahindra Thar DI )  + ( -0.45807850428936653 )*( Car_Model_Mahindra Verito 1.5 )  + ( 0.06253010183649056 )*( Car_Model_Mahindra Verito Vibe )  + ( 0.3781528228284638 )*( Car_Model_Mahindra XUV300 W8 )  + ( 0.5623220521056226 )*( Car_Model_Mahindra XUV500 AT )  + ( 0.5649329558583039 )*( Car_Model_Mahindra XUV500 W10 )  + ( 0.3434364841613003 )*( Car_Model_Mahindra XUV500 W4 )  + ( 0.1814909919265857 )*( Car_Model_Mahindra XUV500 W6 )  + ( 9.504241837987593e-11 )*( Car_Model_Mahindra XUV500 W7 )  + ( 0.30734371539963545 )*( Car_Model_Mahindra XUV500 W8 )  + ( 0.4219259119418504 )*( Car_Model_Mahindra XUV500 W9 )  + ( -0.5850992259669425 )*( Car_Model_Mahindra Xylo D2 )  + ( -0.26253483617208984 )*( Car_Model_Mahindra Xylo D4 )  + ( -0.45954482546471365 )*( Car_Model_Mahindra Xylo E2 )  + ( -0.16413895713501148 )*( Car_Model_Mahindra Xylo E4 )  + ( 0.15728705558790776 )*( Car_Model_Mahindra Xylo E8 )  + ( 0.008028166981353516 )*( Car_Model_Mahindra Xylo E9 )  + ( -0.11998352756786956 )*( Car_Model_Mahindra Xylo H4 )  + ( 8.979312779433535e-11 )*( Car_Model_Mahindra Xylo H9 )  + ( -0.20253741152684268 )*( Car_Model_Maruti 1000 AC )  + ( -0.29858079114461283 )*( Car_Model_Maruti 800 AC )  + ( -0.7452120214516935 )*( Car_Model_Maruti 800 DX )  + ( -0.9591323827939662 )*( Car_Model_Maruti 800 Std )  + ( -0.22469776672678773 )*( Car_Model_Maruti A-Star AT )  + ( -0.35804577846553864 )*( Car_Model_Maruti A-Star Lxi )  + ( -0.022414785008011962 )*( Car_Model_Maruti A-Star Vxi )  + ( 0.8652994399072697 )*( Car_Model_Maruti A-Star Zxi )  + ( -0.4421593670642558 )*( Car_Model_Maruti Alto 800 )  + ( -0.4065669682591391 )*( Car_Model_Maruti Alto Green )  + ( -0.3672604521927608 )*( Car_Model_Maruti Alto K10 )  + ( -0.17689577893924707 )*( Car_Model_Maruti Alto LX )  + ( 0.5516459694644711 )*( Car_Model_Maruti Alto LXI )  + ( -0.14732671760706975 )*( Car_Model_Maruti Alto LXi )  + ( -0.535225587442202 )*( Car_Model_Maruti Alto Std )  + ( 5.983191719849401e-11 )*( Car_Model_Maruti Alto VXi )  + ( -1.7880391611768687e-11 )*( Car_Model_Maruti Alto Vxi )  + ( 1.5630857719273195e-11 )*( Car_Model_Maruti Alto XCITE )  + ( 0.20087773697835987 )*( Car_Model_Maruti Baleno Alpha )  + ( 0.05715757400059475 )*( Car_Model_Maruti Baleno Delta )  + ( -0.3713856383138361 )*( Car_Model_Maruti Baleno LXI )  + ( 0.20534700348476276 )*( Car_Model_Maruti Baleno RS )  + ( 0.0012541387598106503 )*( Car_Model_Maruti Baleno Sigma )  + ( 0.036907375442821 )*( Car_Model_Maruti Baleno Vxi )  + ( 0.10605046037952467 )*( Car_Model_Maruti Baleno Zeta )  + ( -0.09049942330363407 )*( Car_Model_Maruti Celerio CNG )  + ( -0.4525967878672484 )*( Car_Model_Maruti Celerio LDi )  + ( -0.27085987573643566 )*( Car_Model_Maruti Celerio LXI )  + ( -0.22186949703301173 )*( Car_Model_Maruti Celerio VXI )  + ( -1.2417192274405409e-10 )*( Car_Model_Maruti Celerio X )  + ( -0.16919443097063297 )*( Car_Model_Maruti Celerio ZDi )  + ( -0.16878550781127113 )*( Car_Model_Maruti Celerio ZXI )  + ( 0.2710371887094253 )*( Car_Model_Maruti Ciaz 1.3 )  + ( 0.25870790576752534 )*( Car_Model_Maruti Ciaz 1.4 )  + ( 0.266736341815147 )*( Car_Model_Maruti Ciaz AT )  + ( 0.33453671111493677 )*( Car_Model_Maruti Ciaz Alpha )  + ( 0.33919805124801294 )*( Car_Model_Maruti Ciaz RS )  + ( 0.2649564211114473 )*( Car_Model_Maruti Ciaz VDI )  + ( 0.23802394150194958 )*( Car_Model_Maruti Ciaz VDi )  + ( 0.2137852906758063 )*( Car_Model_Maruti Ciaz VXi )  + ( 0.3378347232803016 )*( Car_Model_Maruti Ciaz ZDi )  + ( 0.2884973892404153 )*( Car_Model_Maruti Ciaz ZXi )  + ( 0.3737442291320285 )*( Car_Model_Maruti Ciaz Zeta )  + ( -0.03729432582465791 )*( Car_Model_Maruti Dzire AMT )  + ( -0.10113252090179736 )*( Car_Model_Maruti Dzire LDI )  + ( 0.27439949787167434 )*( Car_Model_Maruti Dzire New )  + ( 0.16695881017524483 )*( Car_Model_Maruti Dzire VDI )  + ( 0.22367140631699525 )*( Car_Model_Maruti Dzire VXI )  + ( 0.30366852303389474 )*( Car_Model_Maruti Dzire ZDI )  + ( -0.15765252370086116 )*( Car_Model_Maruti Eeco 5 )  + ( -0.33211711932469534 )*( Car_Model_Maruti Eeco 7 )  + ( -0.22634306591749057 )*( Car_Model_Maruti Eeco CNG )  + ( -0.36103602265961127 )*( Car_Model_Maruti Eeco Smiles )  + ( 3.804609405300141e-12 )*( Car_Model_Maruti Ertiga LXI )  + ( 0.31375093975220625 )*( Car_Model_Maruti Ertiga Paseo )  + ( 0.3036515608141033 )*( Car_Model_Maruti Ertiga SHVS )  + ( 0.39650643399925284 )*( Car_Model_Maruti Ertiga VDI )  + ( 0.2928888852347981 )*( Car_Model_Maruti Ertiga VXI )  + ( 0.4772369164775621 )*( Car_Model_Maruti Ertiga ZDI )  + ( 0.3361923734406641 )*( Car_Model_Maruti Ertiga ZXI )  + ( -0.673947354010268 )*( Car_Model_Maruti Esteem LX )  + ( -0.2825987837616323 )*( Car_Model_Maruti Esteem Vxi )  + ( -0.4639290639924999 )*( Car_Model_Maruti Estilo LXI )  + ( 0.37603973239937927 )*( Car_Model_Maruti Grand Vitara )  + ( -0.12628023447866704 )*( Car_Model_Maruti Ignis 1.2 )  + ( 0.009403624819396378 )*( Car_Model_Maruti Ignis 1.3 )  + ( -0.24669759098393776 )*( Car_Model_Maruti Omni 5 )  + ( -0.6705969265442044 )*( Car_Model_Maruti Omni 8 )  + ( -0.5391862419698145 )*( Car_Model_Maruti Omni E )  + ( -0.27552416842378274 )*( Car_Model_Maruti Omni MPI )  + ( -0.186153332917211 )*( Car_Model_Maruti Ritz AT )  + ( 0.06189674439296626 )*( Car_Model_Maruti Ritz LDi )  + ( -0.01406663142388697 )*( Car_Model_Maruti Ritz LXI )  + ( 0.06642009683188344 )*( Car_Model_Maruti Ritz LXi )  + ( -0.07803440697557326 )*( Car_Model_Maruti Ritz VDI )  + ( -0.055879955195141384 )*( Car_Model_Maruti Ritz VDi )  + ( -0.19851594521936392 )*( Car_Model_Maruti Ritz VXI )  + ( -0.15175062932276828 )*( Car_Model_Maruti Ritz VXi )  + ( -0.027353802462496364 )*( Car_Model_Maruti Ritz ZDi )  + ( 0.061711472891698885 )*( Car_Model_Maruti Ritz ZXI )  + ( 0.46348324169378313 )*( Car_Model_Maruti Ritz ZXi )  + ( 0.436662029613986 )*( Car_Model_Maruti S Cross )  + ( 0.44592882886732 )*( Car_Model_Maruti S-Cross Alpha )  + ( 0.3820078200367477 )*( Car_Model_Maruti S-Cross Delta )  + ( -1.2467804566540508e-13 )*( Car_Model_Maruti S-Cross Zeta )  + ( -0.2877676890056891 )*( Car_Model_Maruti SX4 Green )  + ( 0.42092869182925474 )*( Car_Model_Maruti SX4 S )  + ( 0.17545754772347763 )*( Car_Model_Maruti SX4 VDI )  + ( -0.08269071314272272 )*( Car_Model_Maruti SX4 Vxi )  + ( 0.09972542845325734 )*( Car_Model_Maruti SX4 ZDI )  + ( -0.04847711639332693 )*( Car_Model_Maruti SX4 ZXI )  + ( 0.07674190084356851 )*( Car_Model_Maruti SX4 Zxi )  + ( -0.0064094680923188225 )*( Car_Model_Maruti Swift 1.3 )  + ( 0.12296468041440628 )*( Car_Model_Maruti Swift AMT )  + ( 0.11031619219584837 )*( Car_Model_Maruti Swift DDiS )  + ( 0.1342873025113658 )*( Car_Model_Maruti Swift Dzire )  + ( 0.11845849252612156 )*( Car_Model_Maruti Swift LDI )  + ( 0.0737928705771433 )*( Car_Model_Maruti Swift LXI )  + ( -0.008688050624961289 )*( Car_Model_Maruti Swift LXi )  + ( -0.009369596961013454 )*( Car_Model_Maruti Swift Ldi )  + ( 0.06615895640623369 )*( Car_Model_Maruti Swift Lxi )  + ( 0.15807888565030437 )*( Car_Model_Maruti Swift RS )  + ( 0.09736630773357957 )*( Car_Model_Maruti Swift VDI )  + ( 0.42335235244952785 )*( Car_Model_Maruti Swift VDi )  + ( 0.03169568037394786 )*( Car_Model_Maruti Swift VVT )  + ( 0.014633348974277538 )*( Car_Model_Maruti Swift VXI )  + ( -0.00825905117222863 )*( Car_Model_Maruti Swift VXi )  + ( 0.23111073583904904 )*( Car_Model_Maruti Swift Vdi )  + ( 0.014751832098523604 )*( Car_Model_Maruti Swift ZDI )  + ( 0.15118496221048122 )*( Car_Model_Maruti Swift ZDi )  + ( 0.15646782385958583 )*( Car_Model_Maruti Swift ZXI )  + ( 0.46508926630047565 )*( Car_Model_Maruti Versa DX2 )  + ( 0.3295960764126852 )*( Car_Model_Maruti Vitara Brezza )  + ( -0.20080407125040756 )*( Car_Model_Maruti Wagon R )  + ( -0.1753416967045822 )*( Car_Model_Maruti Zen Estilo )  + ( -0.5325213553114787 )*( Car_Model_Maruti Zen LX )  + ( -0.5322082876600294 )*( Car_Model_Maruti Zen LXI )  + ( -0.5475169494142522 )*( Car_Model_Maruti Zen LXi )  + ( 6.345091119186463e-11 )*( Car_Model_Maruti Zen VX )  + ( -0.46974228342041835 )*( Car_Model_Maruti Zen VXI )  + ( -2.596749204553106e-11 )*( Car_Model_Maruti Zen VXi )  + ( -0.8413439707424932 )*( Car_Model_Mercedes-Benz A Class )  + ( -0.6027528079252052 )*( Car_Model_Mercedes-Benz B Class )  + ( 0.26634026410257955 )*( Car_Model_Mercedes-Benz C-Class Progressive )  + ( -0.008336580569378707 )*( Car_Model_Mercedes-Benz CLA 200 )  + ( -1.3359551604540056 )*( Car_Model_Mercedes-Benz CLA 45 )  + ( 0.5365908771592843 )*( Car_Model_Mercedes-Benz CLS-Class 2006-2010 )  + ( 0.1408643785375836 )*( Car_Model_Mercedes-Benz E-Class 200 )  + ( -0.18958402072892902 )*( Car_Model_Mercedes-Benz E-Class 2009-2013 )  + ( -0.03522692889729668 )*( Car_Model_Mercedes-Benz E-Class 2015-2017 )  + ( 5.472371167325463e-11 )*( Car_Model_Mercedes-Benz E-Class 220 )  + ( -0.38623647931224947 )*( Car_Model_Mercedes-Benz E-Class 230 )  + ( -0.5860233704109807 )*( Car_Model_Mercedes-Benz E-Class 250 )  + ( -0.16536316949409904 )*( Car_Model_Mercedes-Benz E-Class 280 )  + ( 0.08014624007894458 )*( Car_Model_Mercedes-Benz E-Class E )  + ( -0.18503563961917574 )*( Car_Model_Mercedes-Benz E-Class E240 )  + ( -0.1877423034445991 )*( Car_Model_Mercedes-Benz E-Class E250 )  + ( -0.724946434333337 )*( Car_Model_Mercedes-Benz E-Class E270 )  + ( 0.3639846726938029 )*( Car_Model_Mercedes-Benz E-Class E350 )  + ( 0.8818947934089936 )*( Car_Model_Mercedes-Benz E-Class E400 )  + ( 0.4375612432177519 )*( Car_Model_Mercedes-Benz E-Class Facelift )  + ( 0.3962517940926713 )*( Car_Model_Mercedes-Benz GL-Class 2007 )  + ( -0.3249263897918077 )*( Car_Model_Mercedes-Benz GL-Class 350 )  + ( -0.30215398114581504 )*( Car_Model_Mercedes-Benz GLA Class )  + ( 0.46009987123074436 )*( Car_Model_Mercedes-Benz GLC 220 )  + ( 0.1360979457166659 )*( Car_Model_Mercedes-Benz GLC 220d )  + ( 0.7676586115916542 )*( Car_Model_Mercedes-Benz GLC 43 )  + ( 0.05548533852791782 )*( Car_Model_Mercedes-Benz GLE 250d )  + ( 0.7710456616448269 )*( Car_Model_Mercedes-Benz GLE 350d )  + ( 0.8286863958913235 )*( Car_Model_Mercedes-Benz GLS 350d )  + ( 0.17168017085831408 )*( Car_Model_Mercedes-Benz M-Class ML )  + ( -0.23052396339953019 )*( Car_Model_Mercedes-Benz New C-Class )  + ( 0.023156511684418943 )*( Car_Model_Mercedes-Benz R-Class R350 )  + ( 0.039300576629985054 )*( Car_Model_Mercedes-Benz S Class )  + ( -5.6069371368039356e-11 )*( Car_Model_Mercedes-Benz S-Class 280 )  + ( 0.2512643284291042 )*( Car_Model_Mercedes-Benz S-Class 320 )  + ( -1.8985021887907294e-11 )*( Car_Model_Mercedes-Benz S-Class S )  + ( 0.47996827619179633 )*( Car_Model_Mercedes-Benz SL-Class SL )  + ( -1.7861621415804105 )*( Car_Model_Mercedes-Benz SLC 43 )  + ( 0.956158069061055 )*( Car_Model_Mercedes-Benz SLK-Class 55 )  + ( 0.562181307841286 )*( Car_Model_Mercedes-Benz SLK-Class SLK )  + ( -0.3187793371108433 )*( Car_Model_Mini Clubman Cooper )  + ( -0.28874191868465854 )*( Car_Model_Mini Cooper 3 )  + ( 0.4109247239143866 )*( Car_Model_Mini Cooper 5 )  + ( 0.5776263148378017 )*( Car_Model_Mini Cooper Convertible )  + ( -0.09945322944010976 )*( Car_Model_Mini Cooper Countryman )  + ( 0.0830319769104905 )*( Car_Model_Mini Cooper S )  + ( 0.1124673188211498 )*( Car_Model_Mini Countryman Cooper )  + ( -0.42985200821271075 )*( Car_Model_Mitsubishi Cedia Sports )  + ( -0.6182714402661322 )*( Car_Model_Mitsubishi Lancer 1.5 )  + ( 0.24845237802297596 )*( Car_Model_Mitsubishi Lancer GLXD )  + ( 4.542383136296735e-11 )*( Car_Model_Mitsubishi Montero 3.2 )  + ( 0.19397648210969226 )*( Car_Model_Mitsubishi Outlander 2.4 )  + ( 0.2847269412601452 )*( Car_Model_Mitsubishi Pajero 2.8 )  + ( 5.061923102900323e-11 )*( Car_Model_Mitsubishi Pajero 4X4 )  + ( 0.140123941338034 )*( Car_Model_Mitsubishi Pajero Sport )  + ( 3.5251079832931964e-11 )*( Car_Model_Nissan 370Z AT )  + ( -0.2015894222483573 )*( Car_Model_Nissan Evalia 2013 )  + ( -0.34029129795589674 )*( Car_Model_Nissan Micra Active )  + ( -0.1886836178636765 )*( Car_Model_Nissan Micra Diesel )  + ( -0.35880235997481996 )*( Car_Model_Nissan Micra XE )  + ( -0.5373866280420782 )*( Car_Model_Nissan Micra XL )  + ( -0.3141729686617493 )*( Car_Model_Nissan Micra XV )  + ( -0.09999345780852374 )*( Car_Model_Nissan Sunny 2011-2014 )  + ( 0.10606060230134941 )*( Car_Model_Nissan Sunny Diesel )  + ( 5.681410897295791e-11 )*( Car_Model_Nissan Sunny XE )  + ( 0.22560599887174176 )*( Car_Model_Nissan Sunny XL )  + ( -0.021386796668035103 )*( Car_Model_Nissan Sunny XV )  + ( -0.012109783961947873 )*( Car_Model_Nissan Teana 230jM )  + ( -1.0676282080623878e-10 )*( Car_Model_Nissan Teana XL )  + ( 0.26335947253790215 )*( Car_Model_Nissan Teana XV )  + ( -1.2298495555285172e-11 )*( Car_Model_Nissan Terrano XE )  + ( 0.1321078106733146 )*( Car_Model_Nissan Terrano XL )  + ( 0.19692496923116948 )*( Car_Model_Nissan Terrano XV )  + ( 0.6285585923941868 )*( Car_Model_Nissan X-Trail SLX )  + ( 0.15119066469132675 )*( Car_Model_OpelCorsa 1.4Gsi )  + ( -1.440827457344085e-10 )*( Car_Model_Porsche Boxster S )  + ( -0.03853372816318126 )*( Car_Model_Porsche Cayenne 2009-2014 )  + ( 2.5242141710180022e-11 )*( Car_Model_Porsche Cayenne Base )  + ( -0.4162925631223386 )*( Car_Model_Porsche Cayenne Diesel )  + ( 0.35421957271631854 )*( Car_Model_Porsche Cayenne S )  + ( 0.18201160965558263 )*( Car_Model_Porsche Cayenne Turbo )  + ( 0.4551114919368335 )*( Car_Model_Porsche Cayman 2009-2012 )  + ( 2.2626678308768078e-11 )*( Car_Model_Porsche Panamera 2010 )  + ( 0.6644052384250455 )*( Car_Model_Porsche Panamera Diesel )  + ( 0.5617009295363118 )*( Car_Model_Renault Captur 1.5 )  + ( 0.33733414006915674 )*( Car_Model_Renault Duster 110PS )  + ( 0.2016950461067708 )*( Car_Model_Renault Duster 85PS )  + ( 0.21482816466809823 )*( Car_Model_Renault Duster Adventure )  + ( 0.00098122435023878 )*( Car_Model_Renault Duster Petrol )  + ( 0.3697832159178095 )*( Car_Model_Renault Duster RXZ )  + ( 9.573132564444364e-11 )*( Car_Model_Renault Fluence 1.5 )  + ( 1.0271901862416084e-10 )*( Car_Model_Renault Fluence 2.0 )  + ( -0.00876135945440893 )*( Car_Model_Renault Fluence Diesel )  + ( -0.5155949095301671 )*( Car_Model_Renault KWID 1.0 )  + ( -0.7290593548438632 )*( Car_Model_Renault KWID AMT )  + ( -0.40704474014849656 )*( Car_Model_Renault KWID Climber )  + ( -0.5501546473906863 )*( Car_Model_Renault KWID RXL )  + ( -0.5857866144247161 )*( Car_Model_Renault KWID RXT )  + ( 0.5990760913595589 )*( Car_Model_Renault Koleos 2.0 )  + ( 0.010141415821319763 )*( Car_Model_Renault Koleos 4X2 )  + ( 0.21300186750368963 )*( Car_Model_Renault Lodgy 110PS )  + ( -0.16011141109746552 )*( Car_Model_Renault Pulse Petrol )  + ( -0.06842409751380008 )*( Car_Model_Renault Pulse RxL )  + ( 0.18747672070624083 )*( Car_Model_Renault Pulse RxZ )  + ( 0.025807386273647898 )*( Car_Model_Renault Scala Diesel )  + ( -0.2842619060046638 )*( Car_Model_Renault Scala RxL )  + ( -0.7088660227001243 )*( Car_Model_Skoda Fabia 1.2 )  + ( -0.4421027445758553 )*( Car_Model_Skoda Fabia 1.2L )  + ( -0.31628556639037725 )*( Car_Model_Skoda Fabia 1.4 )  + ( -0.8190120079677364 )*( Car_Model_Skoda Fabia 1.6 )  + ( -0.05686890492990572 )*( Car_Model_Skoda Laura 1.8 )  + ( 0.07794173179288617 )*( Car_Model_Skoda Laura 1.9 )  + ( -3.515951782320087e-05 )*( Car_Model_Skoda Laura Ambiente )  + ( 0.0008483872594788958 )*( Car_Model_Skoda Laura Ambition )  + ( -0.43581475070472087 )*( Car_Model_Skoda Laura Classic )  + ( -0.080748145682343 )*( Car_Model_Skoda Laura Elegance )  + ( -0.0783972806702582 )*( Car_Model_Skoda Laura L )  + ( 0.08622116590485444 )*( Car_Model_Skoda Laura RS )  + ( -0.5308561373214752 )*( Car_Model_Skoda Octavia 1.9 )  + ( 0.04894423918966746 )*( Car_Model_Skoda Octavia 2.0 )  + ( -0.31480807858078536 )*( Car_Model_Skoda Octavia Ambiente )  + ( 0.6178188820994455 )*( Car_Model_Skoda Octavia Ambition )  + ( -0.03532699179717795 )*( Car_Model_Skoda Octavia Classic )  + ( 0.6180002835643451 )*( Car_Model_Skoda Octavia Elegance )  + ( -0.2691061383696805 )*( Car_Model_Skoda Octavia L )  + ( -0.41108691971699585 )*( Car_Model_Skoda Octavia RS )  + ( -0.4340752808468018 )*( Car_Model_Skoda Octavia Rider )  + ( 0.6574009258908589 )*( Car_Model_Skoda Octavia Style )  + ( -0.1146617923627596 )*( Car_Model_Skoda Rapid 1.5 )  + ( -0.19292846942735298 )*( Car_Model_Skoda Rapid 1.6 )  + ( -1.687157358265523e-12 )*( Car_Model_Skoda Rapid 2013-2016 )  + ( 1.1350228686069462e-11 )*( Car_Model_Skoda Rapid Leisure )  + ( -0.20261321096109955 )*( Car_Model_Skoda Rapid Ultima )  + ( 0.22595110958789938 )*( Car_Model_Skoda Superb 1.8 )  + ( 0.1735410159858114 )*( Car_Model_Skoda Superb 2.5 )  + ( 0.07386134495170449 )*( Car_Model_Skoda Superb 2.8 )  + ( 0.8480230903596483 )*( Car_Model_Skoda Superb 2009-2014 )  + ( -0.161110103188054 )*( Car_Model_Skoda Superb 3.6 )  + ( 4.4800940735001404e-11 )*( Car_Model_Skoda Superb Ambition )  + ( 0.314700646086892 )*( Car_Model_Skoda Superb Elegance )  + ( 0.2528827811561284 )*( Car_Model_Skoda Superb L&K )  + ( 0.295907949663559 )*( Car_Model_Skoda Superb Petrol )  + ( 0.42478899575373763 )*( Car_Model_Skoda Superb Style )  + ( 0.22314350407451522 )*( Car_Model_Skoda Yeti Ambition )  + ( 0.3446360257051571 )*( Car_Model_Skoda Yeti Elegance )  + ( -0.2064055966065449 )*( Car_Model_Smart Fortwo CDI )  + ( 0.018355207647474378 )*( Car_Model_Tata Bolt Quadrajet )  + ( -1.5215328996731614e-11 )*( Car_Model_Tata Bolt Revotron )  + ( 1.0047888060484478 )*( Car_Model_Tata Hexa XT )  + ( 0.9960411028102205 )*( Car_Model_Tata Hexa XTA )  + ( -0.6330960495958644 )*( Car_Model_Tata Indica DLS )  + ( -7.407519042601507e-12 )*( Car_Model_Tata Indica GLS )  + ( -0.47699605256232613 )*( Car_Model_Tata Indica LEI )  + ( -0.5219396131439005 )*( Car_Model_Tata Indica V2 )  + ( 0.16261805910295024 )*( Car_Model_Tata Indica Vista )  + ( -0.1367669172924717 )*( Car_Model_Tata Indigo CS )  + ( -0.14845988777841734 )*( Car_Model_Tata Indigo GLE )  + ( -0.3760095280119166 )*( Car_Model_Tata Indigo LS )  + ( -0.42311996036481364 )*( Car_Model_Tata Indigo LX )  + ( -0.5707294833897029 )*( Car_Model_Tata Indigo XL )  + ( -0.24073759714639398 )*( Car_Model_Tata Indigo eCS )  + ( 0.17946887411085014 )*( Car_Model_Tata Manza Aqua )  + ( -0.18501274064204343 )*( Car_Model_Tata Manza Aura )  + ( -0.08199101295189587 )*( Car_Model_Tata Manza Club )  + ( 0.1466247723663538 )*( Car_Model_Tata Manza ELAN )  + ( -0.9232724892908419 )*( Car_Model_Tata Nano CX )  + ( -0.07278201317581476 )*( Car_Model_Tata Nano Cx )  + ( -0.8557226430461945 )*( Car_Model_Tata Nano LX )  + ( -0.599679665608608 )*( Car_Model_Tata Nano Lx )  + ( -2.3141488725286763e-12 )*( Car_Model_Tata Nano STD )  + ( -0.6602420191575313 )*( Car_Model_Tata Nano Twist )  + ( -0.5300614755124052 )*( Car_Model_Tata Nano XT )  + ( -0.1278340806648117 )*( Car_Model_Tata Nano XTA )  + ( 0.1647446484617877 )*( Car_Model_Tata New Safari )  + ( -5.518918655411653e-12 )*( Car_Model_Tata Nexon 1.2 )  + ( 0.654688090713141 )*( Car_Model_Tata Nexon 1.5 )  + ( 0.35350363517678546 )*( Car_Model_Tata Safari DICOR )  + ( 0.7327516357065286 )*( Car_Model_Tata Safari Storme )  + ( 0.4428582645779092 )*( Car_Model_Tata Sumo DX )  + ( 0.2221817079973325 )*( Car_Model_Tata Sumo Delux )  + ( 0.2552212320337284 )*( Car_Model_Tata Sumo EX )  + ( -1.103311886296865e-11 )*( Car_Model_Tata Tiago 1.05 )  + ( 0.0805104205661551 )*( Car_Model_Tata Tiago 1.2 )  + ( 0.0506920675426199 )*( Car_Model_Tata Tiago AMT )  + ( -0.004688931979962774 )*( Car_Model_Tata Tiago Wizz )  + ( 0.3347890776669569 )*( Car_Model_Tata Tigor 1.05 )  + ( 0.1408806608355754 )*( Car_Model_Tata Tigor 1.2 )  + ( 0.17266267420635864 )*( Car_Model_Tata Tigor XE )  + ( -1.6432924188857816e-12 )*( Car_Model_Tata Venture EX )  + ( 0.21455904461428746 )*( Car_Model_Tata Xenon XT )  + ( 0.10901846905990811 )*( Car_Model_Tata Zest Quadrajet )  + ( 0.22567654491263756 )*( Car_Model_Tata Zest Revotron )  + ( 0.3380167262070874 )*( Car_Model_Toyota Camry 2.5 )  + ( -8.745060231518664e-12 )*( Car_Model_Toyota Camry A/T )  + ( 0.816743879953526 )*( Car_Model_Toyota Camry Hybrid )  + ( -6.190881141066029e-13 )*( Car_Model_Toyota Camry MT )  + ( -0.17886121605961916 )*( Car_Model_Toyota Camry W2 )  + ( -0.285193668203089 )*( Car_Model_Toyota Camry W4 )  + ( 0.10681784462096029 )*( Car_Model_Toyota Corolla 1.8 )  + ( 0.025411935611697523 )*( Car_Model_Toyota Corolla Altis )  + ( 2.5471215645022673e-12 )*( Car_Model_Toyota Corolla DX )  + ( 0.19501452076374445 )*( Car_Model_Toyota Corolla Executive )  + ( -0.5482237036121994 )*( Car_Model_Toyota Corolla H2 )  + ( -0.3648218318941853 )*( Car_Model_Toyota Corolla H4 )  + ( -0.22693547985427318 )*( Car_Model_Toyota Corolla H5 )  + ( -0.37453631832301987 )*( Car_Model_Toyota Etios 1.4 )  + ( -0.4331667595085384 )*( Car_Model_Toyota Etios Cross )  + ( -0.45890643107636087 )*( Car_Model_Toyota Etios G )  + ( -0.35860649565384967 )*( Car_Model_Toyota Etios GD )  + ( -0.553391747085408 )*( Car_Model_Toyota Etios Liva )  + ( -2.2666313270747196e-12 )*( Car_Model_Toyota Etios Petrol )  + ( -0.41480606001468007 )*( Car_Model_Toyota Etios V )  + ( -0.30957135832793203 )*( Car_Model_Toyota Etios VD )  + ( -0.530249954023722 )*( Car_Model_Toyota Etios VX )  + ( 1.8491319586644295e-12 )*( Car_Model_Toyota Etios VXD )  + ( 0.6956068779076915 )*( Car_Model_Toyota Fortuner 2.8 )  + ( 0.47942222010512037 )*( Car_Model_Toyota Fortuner 3.0 )  + ( 0.5310458852951082 )*( Car_Model_Toyota Fortuner 4x2 )  + ( 0.6042188461538419 )*( Car_Model_Toyota Fortuner 4x4 )  + ( 0.669937476118358 )*( Car_Model_Toyota Fortuner TRD )  + ( 0.2005932552987394 )*( Car_Model_Toyota Innova 2.0 )  + ( 0.18743002728743402 )*( Car_Model_Toyota Innova 2.5 )  + ( 0.23876830305005028 )*( Car_Model_Toyota Innova Crysta )  + ( -0.4704998627306868 )*( Car_Model_Toyota Land Cruiser )  + ( -0.3702847761062343 )*( Car_Model_Toyota Platinum Etios )  + ( 1.3877787807814457e-16 )*( Car_Model_Toyota Prius 2009-2016 )  + ( 0.4293646644278223 )*( Car_Model_Toyota Qualis FS )  + ( 2.0896726325237217e-17 )*( Car_Model_Toyota Qualis Fleet )  + ( 0.23550080865495826 )*( Car_Model_Toyota Qualis RS )  + ( -0.3039445188593465 )*( Car_Model_Volkswagen Ameo 1.2 )  + ( -0.3194234123137757 )*( Car_Model_Volkswagen Ameo 1.5 )  + ( 0.0 )*( Car_Model_Volkswagen Beetle 2.0 )  + ( 0.029359872132089952 )*( Car_Model_Volkswagen CrossPolo 1.2 )  + ( -0.19968480992867751 )*( Car_Model_Volkswagen CrossPolo 1.5 )  + ( 0.2503594784924259 )*( Car_Model_Volkswagen Jetta 2007-2011 )  + ( 0.3379534234416141 )*( Car_Model_Volkswagen Jetta 2012-2014 )  + ( 0.377829662029187 )*( Car_Model_Volkswagen Jetta 2013-2015 )  + ( 0.07195391486339026 )*( Car_Model_Volkswagen Passat 1.8 )  + ( -0.006033752881467772 )*( Car_Model_Volkswagen Passat 2.0 )  + ( 0.310316719141447 )*( Car_Model_Volkswagen Passat Diesel )  + ( 0.3871615752021803 )*( Car_Model_Volkswagen Passat Highline )  + ( -0.21807732944828778 )*( Car_Model_Volkswagen Polo 1.0 )  + ( -0.23280729998905128 )*( Car_Model_Volkswagen Polo 1.2 )  + ( -0.22392618170857656 )*( Car_Model_Volkswagen Polo 1.5 )  + ( 0.0 )*( Car_Model_Volkswagen Polo ALLSTAR )  + ( -0.21245935707161867 )*( Car_Model_Volkswagen Polo Diesel )  + ( -0.06756369440115514 )*( Car_Model_Volkswagen Polo GT )  + ( -0.3016751254123011 )*( Car_Model_Volkswagen Polo GTI )  + ( 0.0 )*( Car_Model_Volkswagen Polo IPL )  + ( -0.2563987747613334 )*( Car_Model_Volkswagen Polo Petrol )  + ( 1.0712607129514522 )*( Car_Model_Volkswagen Tiguan 2.0 )  + ( -0.03280653981357171 )*( Car_Model_Volkswagen Vento 1.2 )  + ( -0.019155124349357622 )*( Car_Model_Volkswagen Vento 1.5 )  + ( -0.11714057203249376 )*( Car_Model_Volkswagen Vento 1.6 )  + ( -0.20793250671940425 )*( Car_Model_Volkswagen Vento 2013-2015 )  + ( -0.04604758676977058 )*( Car_Model_Volkswagen Vento Diesel )  + ( -0.4778732929368683 )*( Car_Model_Volkswagen Vento IPL )  + ( 0.0 )*( Car_Model_Volkswagen Vento Konekt )  + ( 0.0 )*( Car_Model_Volkswagen Vento Magnific )  + ( -0.1361426124931128 )*( Car_Model_Volkswagen Vento Petrol )  + ( -0.03240755181262438 )*( Car_Model_Volkswagen Vento Sport )  + ( 0.16814253471764462 )*( Car_Model_Volkswagen Vento TSI )  + ( 0.0 )*( Car_Model_Volvo S60 D3 )  + ( 0.01467677625841321 )*( Car_Model_Volvo S60 D4 )  + ( 0.279495855842044 )*( Car_Model_Volvo S60 D5 )  + ( -0.35009834200008544 )*( Car_Model_Volvo S80 2006-2013 )  + ( -0.19843162763150027 )*( Car_Model_Volvo S80 D5 )  + ( 0.2321954347668532 )*( Car_Model_Volvo V40 Cross )  + ( 0.25725659466302014 )*( Car_Model_Volvo V40 D3 )  + ( -0.2047931246215524 )*( Car_Model_Volvo XC60 D4 )  + ( 0.35238205895586217 )*( Car_Model_Volvo XC60 D5 )  + ( 0.0 )*( Car_Model_Volvo XC90 2007-2015 )

Insights and Key Takeaways

  • The Brand which sells the most cars is Maruti followed by Hyundai, Honda, Toyota, Mercedes-Benz, Volkswagen, Ford, Mahindra, BMW, Audi
  • The Car Model sold the most is Maruti Swift Dzire, followed by Hyundai Grand i10, Maruti Wagon R, Toyota Innova 2.5, Hyundai Verna 1.6, Honda City 1.5 , Honda City i, Hyundai Creta 1.6, Mercedes-Benz New C-Class, BMW 3 Series.
  • The location which sells the most cars is Mumbai, followed by Hyderabad, Coimbatore, Kochi, and Pune.

  • The age of the cars range from 3 to 26, with a mean of 8.63 and a median of 8.
  • The manufacturing year of the used cars ranges from 1996-2019, with peaks of frequent values at 2015 and 2011.
  • The number of Kilometers driven ranges from 171 to 6,500,000, and the mean value is 58699.06.

  • The values for the Price of the used cars range from 0.44 to 160 INR, with a mean of ~9.479 and a median of 5.64.
  • The values for the Price of a new car of the same model range from 3.91 to 375 INR, with a mean of ~21.30732 and median of 11.3.
  • First-owned cars are the most common followed by Second-owned, Third-owned, then Fourth & Above
  • Mileage values range from 0 to 33.54 with a median of 18.16 and mean of 18.14.

  • The displacement volume of the engine in CC ranges from 72 to 5998, with a mean of 1616.57 and median of 1493.
  • The most frequent value for number of seats (ranging from 0 to 10) is 5 seats followed by 7 seats, 8 seats, 4 seats, 6 seats, 2 seats, 10 seats, 9 seats, 0 seats.
  • The values for the maximum Power of the engine in bhp range from 34.2 to 616, with a mean of 112.765 and median of 94.
  • Diesel is the most frequent fuel type followed by Petrol, CNG, LPG, Electric.
  • There are more cars with Manual transmission than automatic (almost twice as many)